---
title: "Knowledge Boundary of Large Language Models: A Survey (2025)"
description: "This ACL 2025 survey proposes a formal framework of three nested boundaries (universal… 227 references, cited by 19 papers here."
authors: ["Moxin Li", "Yong Zhao", "Wenxuan Zhang", "Shuaiyi Li", "Wenya Xie", "See-Kiong Ng", "Tat-Seng Chua", "Yang Deng"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.256/"
doi: "10.18653/v1/2025.acl-long.256"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2025-knowledge-boundary-large-language-models/"
---
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# Knowledge Boundary of Large Language Models: A Survey

**Authors:** [Moxin Li](https://md.research.elamur.ai/free/en/authors/moxin-li/), [Yong Zhao](https://md.research.elamur.ai/free/en/authors/yong-zhao/), [Wenxuan Zhang](https://md.research.elamur.ai/free/en/authors/wenxuan-zhang/), [Shuaiyi Li](https://md.research.elamur.ai/free/en/authors/shuaiyi-li/), [Wenya Xie](https://md.research.elamur.ai/free/en/authors/wenya-xie/), [See-Kiong Ng](https://md.research.elamur.ai/free/en/authors/see-kiong-ng/), [Tat-Seng Chua](https://md.research.elamur.ai/free/en/authors/tat-seng-chua/) and [Yang Deng](https://md.research.elamur.ai/free/en/authors/yang-deng/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.256/) · [PDF](https://aclanthology.org/2025.acl-long.256.pdf) · [DOI 10.18653/v1/2025.acl-long.256](https://doi.org/10.18653/v1/2025.acl-long.256) · [OpenAlex](https://openalex.org/W4412945727)

**Citations:** 11 citations (OpenAlex, 2026-10-14) · [19](https://md.research.elamur.ai/free/en/papers/li-2025-knowledge-boundary-large-language-models/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models store a great deal of knowledge in their weights, but they still fail to recall or apply some of it. This shows up as fabricated facts, being swayed by misleading context, and weak handling of vague questions. The authors argue that earlier notions of an LLM "knowledge boundary" fall short: the Know-Unknow Quadrant is informal, one formal definition is tied to a single model, and related surveys are either undefined or narrow.

This ACL 2025 survey proposes a formal framework of three nested boundaries (universal, parametric and outward) and derives a four-type knowledge taxonomy from them. It then organizes the literature around three questions:
- why boundaries matter, through the undesired behaviours they cause;
- how boundaries can be identified, using uncertainty estimation, calibration and probing;
- how the resulting problems can be mitigated, with strategies matched to each knowledge type.

It closes with open problems, including broader benchmarks, detectors that generalize across domains, side effects such as over-refusal, and long-form factuality. The framework gives the field a common vocabulary for building more trustworthy LLM systems.

## Key points
- **Three boundaries:**
  - Universal: what humans know and can express as textual QA.
  - Parametric: what is encoded in a given model's parameters.
  - Outward: what can be empirically checked on a limited set of prompts.
- **Four knowledge types:** Prompt-Agnostic Known (PAK), Prompt-Sensitive Known (PSK), Model-Specific Unknown (MSU) and Model-Agnostic Unknown (MAU). They are defined via output probability above or below a threshold ϵ across input-output expressions Q k.
- **Behaviours mapped to types:**
  - PSK: untruthful answers triggered by false or irrelevant context.
  - MSU: factuality hallucinations from missing domain knowledge, outdated data (e.g., LLaMA2 favouring older years) and overconfidence.
  - MAU: answers that are truthful but unwanted, such as random replies to ambiguous queries.
- **Mitigation side effects:** over-refusal and extra cost from clarification rounds, RAG noise or self-correction.
- **Resources:** a paper list at github.com/li-moxin/knowledge_boundary_survey, plus a dataset overview in the appendix.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [National University of Singapore](https://md.research.elamur.ai/free/en/universities/national-university-of-singapore/) — University, Singapore, Singapore · 180 works · 34,304 citations
- [Singapore University of Technology and Design](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/) — University, Singapore, Singapore · 49 works · 11,074 citations
- [Chinese University of Hong Kong](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong/) — University, Hong Kong, China · 126 works · 14,073 citations
- [Singapore Management University](https://md.research.elamur.ai/free/en/universities/singapore-management-university/) — University, Singapore, Singapore · 27 works · 2,382 citations
- [University of Minnesota](https://md.research.elamur.ai/free/en/universities/university-of-minnesota/) — University, Minneapolis, United States · 28 works · 2,184 citations

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## Cite

Cite as: Moxin Li, Yong Zhao, Wenxuan Zhang, Shuaiyi Li, Wenya Xie, See-Kiong Ng, Tat-Seng Chua and Yang Deng. 2025. Knowledge Boundary of Large Language Models: A Survey. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{li2025knowledge,
  title = {{Knowledge Boundary of Large Language Models: A Survey}},
  author = {Moxin Li, Yong Zhao, Wenxuan Zhang, Shuaiyi Li, Wenya Xie, See-Kiong Ng, Tat-Seng Chua and Yang Deng},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.256},
  url = {https://doi.org/10.18653/v1/2025.acl-long.256}
}
```

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## References

227 references, 76 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Vaibhav Adlakha, Shehzaad Dhuliawala, Kaheer Suleman, Harm de Vries, and Siva Reddy. 2022. [TopiOCQA: Open-domain conversational question answering with topic switching](https://md.research.elamur.ai/free/en/papers/adlakha-2022-topiocqa-open-domain-conversational-question/). Transactions of the Association for Computational Linguistics, 10:468– 483.
2. Ayushi Agarwal, Nisarg Patel, Neeraj Varshney, Mihir Parmar, Pavan Mallina, Aryan Bhavin Shah, Srihari Raju Sangaraju, Tirth Patel, Nihar Thakkar, and Chitta Baral. 2023. Can NLP models ’identify’, ’distinguish’, and ’justify’ questions that don’t have a definitive answer? In TrustNLP Workshop at AC… [find](https://scholar.google.com/scholar?q=Ayushi+Agarwal%2C+Nisarg+Patel%2C+Neeraj+Varshney%2C+Mihir+Parmar%2C+Pavan+Mallina%2C+Aryan+Bhavin+Shah%2C+Srihari+Raju+Sangaraju%2C+Tirth+Patel%2C+Nihar+Thakkar%2C+and+Chitta+Baral%2E+2023%2E+Can+NLP+models+%E2%80%99identify%E2%80%99%2C+%E2%80%99d)
3. Gustaf Ahdritz, Tian Qin, Nikhil Vyas, Boaz Barak, and Benjamin L. Edelman. 2024. Distinguishing the knowable from the unknowable with language models. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net. [find](https://scholar.google.com/scholar?q=Gustaf+Ahdritz%2C+Tian+Qin%2C+Nikhil+Vyas%2C+Boaz+Barak%2C+and+Benjamin+L%2E+Edelman%2E+2024%2E+Distinguishing+the+knowable+from+the+unknowable+with+language+models%2E+In+Forty%2Dfirst+International+Conference+on+Machi)
4. Lukas Aichberger, Kajetan Schweighofer, Mykyta Ielanskyi, and Sepp Hochreiter. 2024. How many opinions does your LLM have? improving uncertainty estimation in NLG. In ICLR 2024 Workshop on Secure and Trustworthy Large Language Models. [find](https://scholar.google.com/scholar?q=Lukas+Aichberger%2C+Kajetan+Schweighofer%2C+Mykyta+Ielanskyi%2C+and+Sepp+Hochreiter%2E+2024%2E+How+many+opinions+does+your+LLM+have%3F+improving+uncertainty+estimation+in+NLG%2E+In+ICLR+2024+Workshop+on+Secure+and+)
5. Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, and Mikhail Burtsev. 2021. Building and evaluating open-domain dialogue corpora with clarifying questions. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punt… [find](https://scholar.google.com/scholar?q=Mohammad+Aliannejadi%2C+Julia+Kiseleva%2C+Aleksandr+Chuklin%2C+Jeff+Dalton%2C+and+Mikhail+Burtsev%2E+2021%2E+Building+and+evaluating+open%2Ddomain+dialogue+corpora+with+clarifying+questions%2E+In+Proceedings+of+the+2)
6. Zeyuan Allen-Zhu and Yuanzhi Li. 2024. [Physics of language models: Part 3.1, knowledge storage and extraction](https://md.research.elamur.ai/free/en/papers/allen-zhu-2023-physics-language-models-part-3/). In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net.
7. Alfonso Amayuelas, Kyle Wong, Liangming Pan, Wenhu Chen, and William Yang Wang. 2024. [Knowledge of knowledge: Exploring known-unknowns uncertainty with large language models](https://md.research.elamur.ai/free/en/papers/amayuelas-2024-knowledge-knowledge-exploring-known-unknowns/). In Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16,…
8. Chinmaya Andukuri, Jan-Philipp Fränken, Tobias Gerstenberg, and Noah D. Goodman. 2024. Star-gate: Teaching language models to ask clarifying questions. CoRR, abs/2403.19154. [find](https://scholar.google.com/scholar?q=Chinmaya+Andukuri%2C+Jan%2DPhilipp+Fr%C3%A4nken%2C+Tobias+Gerstenberg%2C+and+Noah+D%2E+Goodman%2E+2024%2E+Star%2Dgate%3A+Teaching+language+models+to+ask+clarifying+questions%2E+CoRR%2C+abs%2F2403%2E19154%2E)
9. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. [Self-rag: Learning to retrieve, generate, and critique through self-reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/). In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net.
10. Amos Azaria and Tom M. Mitchell. 2023. [The internal state of an LLM knows when it’s lying](https://md.research.elamur.ai/free/en/papers/azaria-2023-internal-state-llm-knows-when/). In Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023, pages 967–976. Association for Computational Linguistics.
11. Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, and Xueqi Cheng. 2024. Is factuality decoding a free lunch for llms? evaluation on knowledge editing benchmark. CoRR, abs/2404.00216. — [arXiv:2404.00216](https://arxiv.org/abs/2404.00216) [find](https://scholar.google.com/scholar?q=Baolong+Bi%2C+Shenghua+Liu%2C+Yiwei+Wang%2C+Lingrui+Mei%2C+and+Xueqi+Cheng%2E+2024%2E+Is+factuality+decoding+a+free+lunch+for+llms%3F+evaluation+on+knowledge+editing+benchmark%2E+CoRR%2C+abs%2F2404%2E00216%2E+%E2%80%94+%5BarXiv%3A2404%2E0)
12. Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. 2023. Discovering latent knowledge in language models without supervision. In The Eleventh International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Collin+Burns%2C+Haotian+Ye%2C+Dan+Klein%2C+and+Jacob+Steinhardt%2E+2023%2E+Discovering+latent+knowledge+in+language+models+without+supervision%2E+In+The+Eleventh+International+Conference+on+Learning+Representatio)
13. Arslan Chaudhry, Sridhar Thiagarajan, and Dilan Gorur. 2024. Finetuning language models to emit linguistic expressions of uncertainty. arXiv preprint arXiv:2409.12180. — [arXiv:2409.12180](https://arxiv.org/abs/2409.12180) [find](https://scholar.google.com/scholar?q=Arslan+Chaudhry%2C+Sridhar+Thiagarajan%2C+and+Dilan+Gorur%2E+2024%2E+Finetuning+language+models+to+emit+linguistic+expressions+of+uncertainty%2E+arXiv+preprint+arXiv%3A2409%2E12180%2E+%E2%80%94+%5BarXiv%3A2409%2E12180%5D%28https%3A%2F%2Farx)
14. Canyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, Xifeng Yan, William Yang Wang, Philip Torr, Dawn Song, and Kai Shu. 2024a. Can editing llms inject harm? Preprint, arXiv:2407.20224. — [arXiv:2407.20224](https://arxiv.org/abs/2407.20224) [find](https://scholar.google.com/scholar?q=Canyu+Chen%2C+Baixiang+Huang%2C+Zekun+Li%2C+Zhaorun+Chen%2C+Shiyang+Lai%2C+Xiongxiao+Xu%2C+Jia%2DChen+Gu%2C+Jindong+Gu%2C+Huaxiu+Yao%2C+Chaowei+Xiao%2C+Xifeng+Yan%2C+William+Yang+Wang%2C+Philip+Torr%2C+Dawn+Song%2C+and+Kai+Shu%2E+20)
15. Dingwei Chen, Feiteng Fang, Shiwen Ni, Feng Liang, Ruifeng Xu, Min Yang, and Chengming Li. 2024b. Lower layer matters: Alleviating hallucination via multi-layer fusion contrastive decoding with truthfulness refocused. arXiv preprint arXiv:2408.08769. — [arXiv:2408.08769](https://arxiv.org/abs/2408.08769) [find](https://scholar.google.com/scholar?q=Dingwei+Chen%2C+Feiteng+Fang%2C+Shiwen+Ni%2C+Feng+Liang%2C+Ruifeng+Xu%2C+Min+Yang%2C+and+Chengming+Li%2E+2024b%2E+Lower+layer+matters%3A+Alleviating+hallucination+via+multi%2Dlayer+fusion+contrastive+decoding+with+truthf)
16. Zhengfu He, Kai Chen, and Xipeng Qiu. 2024b. Can AI assistants know what they don’t know? In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. Open-Review.net. [find](https://scholar.google.com/scholar?q=Zhengfu+He%2C+Kai+Chen%2C+and+Xipeng+Qiu%2E+2024b%2E+Can+AI+assistants+know+what+they+don%E2%80%99t+know%3F+In+Forty%2Dfirst+International+Conference+on+Machine+Learning%2C+ICML+2024%2C+Vienna%2C+Austria%2C+July+21%2D27%2C+2024%2E+Ope)
17. Jiuhai Chen and Jonas Mueller. 2024. [Quantifying uncertainty in answers from any language model and enhancing their trustworthiness](https://md.research.elamur.ai/free/en/papers/chen-2024-quantifying-uncertainty-answers-any-language/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5186–5200.
18. John J. Cherian, Isaac Gibbs, and Emmanuel J. Candès. 2024. Large language model validity via enhanced conformal prediction methods. CoRR, abs/2406.09714. [find](https://scholar.google.com/scholar?q=John+J%2E+Cherian%2C+Isaac+Gibbs%2C+and+Emmanuel+J%2E+Cand%C3%A8s%2E+2024%2E+Large+language+model+validity+via+enhanced+conformal+prediction+methods%2E+CoRR%2C+abs%2F2406%2E09714%2E)
19. Liang CHEN, Li Shen, Yang Deng, Xiaoyan Zhao, Bin Liang, and Kam-Fai Wong. 2025. PEARL: Towards permutation-resilient LLMs. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Liang+CHEN%2C+Li+Shen%2C+Yang+Deng%2C+Xiaoyan+Zhao%2C+Bin+Liang%2C+and+Kam%2DFai+Wong%2E+2025%2E+PEARL%3A+Towards+permutation%2Dresilient+LLMs%2E+In+The+Thirteenth+International+Conference+on+Learning+Representations%2E)
20. Lida Chen, Zujie Liang, Xintao Wang, Jiaqing Liang, Yanghua Xiao, Feng Wei, Jinglei Chen, Zhenghong Hao, Bing Han, and Wei Wang. 2024c. Teaching large language models to express knowledge boundary from their own signals. CoRR, abs/2406.10881. [find](https://scholar.google.com/scholar?q=Lida+Chen%2C+Zujie+Liang%2C+Xintao+Wang%2C+Jiaqing+Liang%2C+Yanghua+Xiao%2C+Feng+Wei%2C+Jinglei+Chen%2C+Zhenghong+Hao%2C+Bing+Han%2C+and+Wei+Wang%2E+2024c%2E+Teaching+large+language+models+to+express+knowledge+boundary+fro)
21. Maximillian Chen, Ruoxi Sun, Sercan Ö. Arik, and Tomas Pfister. 2024d. Learning to clarify: Multi-turn conversations with action-based contrastive self-training. ArXiv, abs/2406.00222. — [arXiv:2406.00222](https://arxiv.org/abs/2406.00222) [find](https://scholar.google.com/scholar?q=Maximillian+Chen%2C+Ruoxi+Sun%2C+Sercan+%C3%96%2E+Arik%2C+and+Tomas+Pfister%2E+2024d%2E+Learning+to+clarify%3A+Multi%2Dturn+conversations+with+action%2Dbased+contrastive+self%2Dtraining%2E+ArXiv%2C+abs%2F2406%2E00222%2E+%E2%80%94+%5BarXiv%3A2406%2E0)
22. Shiqi Chen, Miao Xiong, Junteng Liu, Zhengxuan Wu, Teng Xiao, Siyang Gao, and Junxian He. 2024e. In-context sharpness as alerts: An inner representation perspective for hallucination mitigation. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024… [find](https://scholar.google.com/scholar?q=Shiqi+Chen%2C+Miao+Xiong%2C+Junteng+Liu%2C+Zhengxuan+Wu%2C+Teng+Xiao%2C+Siyang+Gao%2C+and+Junxian+He%2E+2024e%2E+In%2Dcontext+sharpness+as+alerts%3A+An+inner+representation+perspective+for+hallucination+mitigation%2E+In+Fo)
23. Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, and Tat-Seng Chua. 2024f. Style: Improving domain transferability of asking clarification questions in large language model powered conversational agents. In Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Yue+Chen%2C+Chen+Huang%2C+Yang+Deng%2C+Wenqiang+Lei%2C+Dingnan+Jin%2C+Jia+Liu%2C+and+Tat%2DSeng+Chua%2E+2024f%2E+Style%3A+Improving+domain+transferability+of+asking+clarification+questions+in+large+language+model+powered)
24. Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema N Moussa, Matthew I. Beane, Ting-Hao ’Kenneth’ Huang, Bryan R. Routledge, and William Yang Wang. 2021. [Finqa: A dataset of numerical reasoning over financial data](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/). ArXiv, abs/2109.00122.
25. Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao, Fengzong Lian, Zhanhui Kang, Di Wang, and Chengzhong Xu. 2024g. Truth forest: Toward multi-scale truthfulness in large language models through intervention without tuning. In Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, pages 20967– 2… [find](https://scholar.google.com/scholar?q=Zhongzhi+Chen%2C+Xingwu+Sun%2C+Xianfeng+Jiao%2C+Fengzong+Lian%2C+Zhanhui+Kang%2C+Di+Wang%2C+and+Chengzhong+Xu%2E+2024g%2E+Truth+forest%3A+Toward+multi%2Dscale+truthfulness+in+large+language+models+through+intervention+wi)
26. Jeffrey Cheng, Marc Marone, Orion Weller, Dawn Lawrie, Daniel Khashabi, and Benjamin Van Durme. 2024a. Dated data: Tracing knowledge cutoffs in large language models. ArXiv, abs/2403.12958. [find](https://scholar.google.com/scholar?q=Jeffrey+Cheng%2C+Marc+Marone%2C+Orion+Weller%2C+Dawn+Lawrie%2C+Daniel+Khashabi%2C+and+Benjamin+Van+Durme%2E+2024a%2E+Dated+data%3A+Tracing+knowledge+cutoffs+in+large+language+models%2E+ArXiv%2C+abs%2F2403%2E12958%2E)
27. Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu, Wenwei Zhang, Zhangyue Yin, Shimin Li, Linyang Li, Yung-Sung Chuang, Yujia Xie, Hongyin Luo, Yoon Kim, James R. Glass, and Pengcheng He. 2024. [Dola: Decoding by contrasting layers improves factuality in large language models](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/). In The Twelfth International…
28. Matthew Dahl, Varun Magesh, Mirac Suzgun, and Daniel E Ho. 2024. [Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/). Journal of Legal Analysis, 16(1):64–93.
29. Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, and Zhiting Hu. 2022. [Rlprompt: Optimizing discrete text prompts with reinforcement learning](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3369–339…
30. Yang Deng, Wenqiang Lei, Wai Lam, and Tat-Seng Chua. 2023a. A survey on proactive dialogue systems: Problems, methods, and prospects. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, pages 6583–6591. [find](https://scholar.google.com/scholar?q=Yang+Deng%2C+Wenqiang+Lei%2C+Wai+Lam%2C+and+Tat%2DSeng+Chua%2E+2023a%2E+A+survey+on+proactive+dialogue+systems%3A+Problems%2C+methods%2C+and+prospects%2E+In+Proceedings+of+the+Thirty%2DSecond+International+Joint+Conference)
31. Yang Deng, Lizi Liao, Liang Chen, Hongru Wang, Wenqiang Lei, and Tat-Seng Chua. 2023b. [Prompting and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration](https://md.research.elamur.ai/free/en/papers/deng-2023-prompting-evaluating-large-language-models/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10602–10…
32. Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, and Tat-Seng Chua. 2024. Don’t just say "i don’t know"! self-aligning large language models for responding to unknown questions with explanations. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miam… [find](https://scholar.google.com/scholar?q=Yang+Deng%2C+Yong+Zhao%2C+Moxin+Li%2C+See%2DKiong+Ng%2C+and+Tat%2DSeng+Chua%2E+2024%2E+Don%E2%80%99t+just+say+%22i+don%E2%80%99t+know%22%21+self%2Daligning+large+language+models+for+responding+to+unknown+questions+with+explanations%2E+In+Proc)
33. Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, LIN Yong, Xiao Zhou, and Tong Zhang. 2023. Black-box prompt learning for pre-trained language models. Transactions on Machine Learning Research. [find](https://scholar.google.com/scholar?q=Shizhe+Diao%2C+Zhichao+Huang%2C+Ruijia+Xu%2C+Xuechun+Li%2C+LIN+Yong%2C+Xiao+Zhou%2C+and+Tong+Zhang%2E+2023%2E+Black%2Dbox+prompt+learning+for+pre%2Dtrained+language+models%2E+Transactions+on+Machine+Learning+Research%2E)
34. Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. 2022. [Calibrating factual knowledge in pretrained language models](https://md.research.elamur.ai/free/en/papers/dong-2022-calibrating-factual-knowledge-pretrained-language/). In Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pages 5937–5947. Association f…
35. Qingxiu Dong, Jingjing Xu, Lingpeng Kong, Zhifang Sui, and Lei Li. 2024. Statistical knowledge assessment for large language models. Advances in Neural Information Processing Systems, 36. [find](https://scholar.google.com/scholar?q=Qingxiu+Dong%2C+Jingjing+Xu%2C+Lingpeng+Kong%2C+Zhifang+Sui%2C+and+Lei+Li%2E+2024%2E+Statistical+knowledge+assessment+for+large+language+models%2E+Advances+in+Neural+Information+Processing+Systems%2C+36%2E)
36. Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. 2024. [Improving factuality and reasoning in language models through multiagent debate](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/). In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net.
37. Jinhao Duan, Hao Cheng, Shiqi Wang, Chenan Wang, Alex Zavalny, Renjing Xu, Bhavya Kailkhura, and Kaidi Xu. 2023. Shifting attention to relevance: Towards the uncertainty estimation of large language models. CoRR, abs/2307.01379. [find](https://scholar.google.com/scholar?q=Jinhao+Duan%2C+Hao+Cheng%2C+Shiqi+Wang%2C+Chenan+Wang%2C+Alex+Zavalny%2C+Renjing+Xu%2C+Bhavya+Kailkhura%2C+and+Kaidi+Xu%2E+2023%2E+Shifting+attention+to+relevance%3A+Towards+the+uncertainty+estimation+of+large+language+m)
38. Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg. 2021. Measuring and improving consistency in pretrained language models. Transactions of the Association for Computational Linguistics, 9:1012–1031. [find](https://scholar.google.com/scholar?q=Yanai+Elazar%2C+Nora+Kassner%2C+Shauli+Ravfogel%2C+Abhilasha+Ravichander%2C+Eduard+Hovy%2C+Hinrich+Sch%C3%BCtze%2C+and+Yoav+Goldberg%2E+2021%2E+Measuring+and+improving+consistency+in+pretrained+language+models%2E+Transactio)
39. Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, and Maxim Panov. 2024. [Fact-checking the output of large language models via token-level uncertainty quantificat…](https://md.research.elamur.ai/free/en/papers/fadeeva-2024-fact-checking-output-large-language/)
40. Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, and Vincent Ng. 2024. [Lawbench: Benchmarking legal knowledge of large language models](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/). In Conference on Empirical Methods in Natural Language Processing.
41. Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, and Yulia Tsvetkov. 2024. Don’t hallucinate, abstain: Identifying LLM knowledge gaps via multi-LLM collaboration. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pape… [find](https://scholar.google.com/scholar?q=Shangbin+Feng%2C+Weijia+Shi%2C+Yike+Wang%2C+Wenxuan+Ding%2C+Vidhisha+Balachandran%2C+and+Yulia+Tsvetkov%2E+2024%2E+Don%E2%80%99t+hallucinate%2C+abstain%3A+Identifying+LLM+knowledge+gaps+via+multi%2DLLM+collaboration%2E+In+Proceedi)
42. Constanza Fierro, Ruchira Dhar, Filippos Stamatiou, Nicolas Garneau, and Anders Søgaard. 2024. Defining knowledge: Bridging epistemology and large language models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2… [find](https://scholar.google.com/scholar?q=Constanza+Fierro%2C+Ruchira+Dhar%2C+Filippos+Stamatiou%2C+Nicolas+Garneau%2C+and+Anders+S%C3%B8gaard%2E+2024%2E+Defining+knowledge%3A+Bridging+epistemology+and+large+language+models%2E+In+Proceedings+of+the+2024+Conferenc)
43. Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata. 2023. [Improving language model negotiation with self-play and in-context learning from ai feedback](https://md.research.elamur.ai/free/en/papers/fu-2023-improving-language-model-negotiation-self/). arXiv preprint arXiv:2305.10142.
44. Chujie Gao, Siyuan Wu, Yue Huang, Dongping Chen, Qihui Zhang, Zhengyan Fu, Yao Wan, Lichao Sun, and Xiangliang Zhang. 2024a. Honestllm: Toward an honest and helpful large language model. Preprint, arXiv:2406.00380. — [arXiv:2406.00380](https://arxiv.org/abs/2406.00380) [find](https://scholar.google.com/scholar?q=Chujie+Gao%2C+Siyuan+Wu%2C+Yue+Huang%2C+Dongping+Chen%2C+Qihui+Zhang%2C+Zhengyan+Fu%2C+Yao+Wan%2C+Lichao+Sun%2C+and+Xiangliang+Zhang%2E+2024a%2E+Honestllm%3A+Toward+an+honest+and+helpful+large+language+model%2E+Preprint%2C+arX)
45. Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2023. [Precise zero-shot dense retrieval without relevance labels](https://md.research.elamur.ai/free/en/papers/gao-2022-precise-zero-shot-dense-retrieval/). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1762–1777, Toronto, Canada. Association for Computational Li…
46. Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, and Kamalika Das. 2024b. Spuq: Perturbation-based uncertainty quantification for large language models. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2336–2346. [find](https://scholar.google.com/scholar?q=Xiang+Gao%2C+Jiaxin+Zhang%2C+Lalla+Mouatadid%2C+and+Kamalika+Das%2E+2024b%2E+Spuq%3A+Perturbation%2Dbased+uncertainty+quantification+for+large+language+models%2E+In+Proceedings+of+the+18th+Conference+of+the+European+)
47. Meiqi Guo, Mingda Zhang, Siva Reddy, and Malihe Alikhani. 2021. Abg-coqa: Clarifying ambiguity in conversational question answering. In 3rd Conference on Automated Knowledge Base Construction, AKBC 2021, Virtual, October 4-8, 2021. [find](https://scholar.google.com/scholar?q=Meiqi+Guo%2C+Mingda+Zhang%2C+Siva+Reddy%2C+and+Malihe+Alikhani%2E+2021%2E+Abg%2Dcoqa%3A+Clarifying+ambiguity+in+conversational+question+answering%2E+In+3rd+Conference+on+Automated+Knowledge+Base+Construction%2C+AKBC+20)
48. Tom Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2023. Aging with GRACE: lifelong model editing with discrete key-value adaptors. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 20… [find](https://scholar.google.com/scholar?q=Tom+Hartvigsen%2C+Swami+Sankaranarayanan%2C+Hamid+Palangi%2C+Yoon+Kim%2C+and+Marzyeh+Ghassemi%2E+2023%2E+Aging+with+GRACE%3A+lifelong+model+editing+with+discrete+key%2Dvalue+adaptors%2E+In+Advances+in+Neural+Informatio)
49. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. [Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps](https://md.research.elamur.ai/free/en/papers/ho-2020-constructing-multi-hop-qa-dataset/). In Proceedings of the 28th International Conference on Computational Linguistics, pages 6609– 6625.
50. Bairu Hou, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang, and Yang Zhang. 2024. Decomposing uncertainty for large language models through input clarification ensembling. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net. [find](https://scholar.google.com/scholar?q=Bairu+Hou%2C+Yujian+Liu%2C+Kaizhi+Qian%2C+Jacob+Andreas%2C+Shiyu+Chang%2C+and+Yang+Zhang%2E+2024%2E+Decomposing+uncertainty+for+large+language+models+through+input+clarification+ensembling%2E+In+Forty%2Dfirst+Internati)
51. Mengting Hu, Zhen Zhang, Shiwan Zhao, Minlie Huang, and Bingzhe Wu. 2023. Uncertainty in natural language processing: Sources, quantification, and applications. arXiv preprint arXiv:2306.04459. — [arXiv:2306.04459](https://arxiv.org/abs/2306.04459) [find](https://scholar.google.com/scholar?q=Mengting+Hu%2C+Zhen+Zhang%2C+Shiwan+Zhao%2C+Minlie+Huang%2C+and+Bingzhe+Wu%2E+2023%2E+Uncertainty+in+natural+language+processing%3A+Sources%2C+quantification%2C+and+applications%2E+arXiv+preprint+arXiv%3A2306%2E04459%2E+%E2%80%94+%5BarX)
52. Chao-Wei Huang and Yun-Nung Chen. 2024a. Factalign: Long-form factuality alignment of large language models. ArXiv, abs/2410.01691. [find](https://scholar.google.com/scholar?q=Chao%2DWei+Huang+and+Yun%2DNung+Chen%2E+2024a%2E+Factalign%3A+Long%2Dform+factuality+alignment+of+large+language+models%2E+ArXiv%2C+abs%2F2410%2E01691%2E)
53. Chao-Wei Huang and Yun-Nung Chen. 2024b. Factalign: Long-form factuality alignment of large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024, pages 16363– 16375. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Chao%2DWei+Huang+and+Yun%2DNung+Chen%2E+2024b%2E+Factalign%3A+Long%2Dform+factuality+alignment+of+large+language+models%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2024%2C+Miami%2C+Florida%2C+U)
54. Hsiu-Yuan Huang, Yutong Yang, Zhaoxi Zhang, Sanwoo Lee, and Yunfang Wu. 2024a. A survey of uncertainty estimation in llms: Theory meets practice. CoRR, abs/2410.15326. — [arXiv:2410.15326](https://arxiv.org/abs/2410.15326) [find](https://scholar.google.com/scholar?q=Hsiu%2DYuan+Huang%2C+Yutong+Yang%2C+Zhaoxi+Zhang%2C+Sanwoo+Lee%2C+and+Yunfang+Wu%2E+2024a%2E+A+survey+of+uncertainty+estimation+in+llms%3A+Theory+meets+practice%2E+CoRR%2C+abs%2F2410%2E15326%2E+%E2%80%94+%5BarXiv%3A2410%2E15326%5D%28https%3A%2F%2Farx)
55. Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. 2023a. [Large language models can self-improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1051–1068.
56. Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2024b. [Large language models cannot self-correct reasoning yet](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-cannot-self/). In The Twelfth International Conference on Learning Representations.
57. Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023b. [Active retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7969–7992, Singapore. Association f…
58. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, and Xinghua Lu. 2019. [Pubmedqa: A dataset for biomedical research question answering](https://md.research.elamur.ai/free/en/papers/jin-2019-pubmedqa-dataset-biomedical-research-question/). In Conference on Empirical Methods in Natural Language Processing.
59. Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. [Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/). arXiv preprint arXiv:1705.03551.
60. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2023b. [A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/). ArXiv, abs/2311.05232.
61. Raviraj Joshi, Kanishk Singla, Anusha Kamath, Raunak Kalani, Rakesh Paul, Utkarsh Vaidya, Sanjay Singh Chauhan, Niranjan Wartikar, and Eileen Long. 2024. Adapting multilingual llms to low-resource languages using continued pre-training and synthetic corpus. ArXiv, abs/2410.14815. — [arXiv:2410.14815](https://arxiv.org/abs/2410.14815) [find](https://scholar.google.com/scholar?q=Raviraj+Joshi%2C+Kanishk+Singla%2C+Anusha+Kamath%2C+Raunak+Kalani%2C+Rakesh+Paul%2C+Utkarsh+Vaidya%2C+Sanjay+Singh+Chauhan%2C+Niranjan+Wartikar%2C+and+Eileen+Long%2E+2024%2E+Adapting+multilingual+llms+to+low%2Dresource+lan)
62. Yuheng Huang, Jiayang Song, Zhijie Wang, Shengming Zhao, Huaming Chen, Felix Juefei-Xu, and Lei Ma. 2023c. Look before you leap: An exploratory study of uncertainty measurement for large language models. arXiv preprint arXiv:2307.10236. — [arXiv:2307.10236](https://arxiv.org/abs/2307.10236) [find](https://scholar.google.com/scholar?q=Yuheng+Huang%2C+Jiayang+Song%2C+Zhijie+Wang%2C+Shengming+Zhao%2C+Huaming+Chen%2C+Felix+Juefei%2DXu%2C+and+Lei+Ma%2E+2023c%2E+Look+before+you+leap%3A+An+exploratory+study+of+uncertainty+measurement+for+large+language+mode)
63. Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. 2022. [Maieutic prompting: Logically consistent reasoning with recursive explanations](https://md.research.elamur.ai/free/en/papers/jung-2022-maieutic-prompting-logically-consistent-reasoning/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1266–1279.
64. Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2023d. [Transformer-patcher: One mistake worth one neuron](https://md.research.elamur.ai/free/en/papers/huang-2023-transformer-patcher-one-mistake-worth/). In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net.
65. Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, and Chenguang Zhu. 2023. In-context demonstration selection with cross entropy difference. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1150– 1162. [find](https://scholar.google.com/scholar?q=Dan+Iter%2C+Reid+Pryzant%2C+Ruochen+Xu%2C+Shuohang+Wang%2C+Yang+Liu%2C+Yichong+Xu%2C+and+Chenguang+Zhu%2E+2023%2E+In%2Dcontext+demonstration+selection+with+cross+entropy+difference%2E+In+Findings+of+the+Association+for+C)
66. Ziwei Ji, Delong Chen, Etsuko Ishii, Samuel Cahyawijaya, Yejin Bang, Bryan Wilie, and Pascale Fung. 2024. [LLM internal states reveal hallucination risk faced with a query](https://md.research.elamur.ai/free/en/papers/ji-2024-llm-internal-states-reveal-hallucination/). In Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 88–104, Miami, Florida…
67. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2023. [Survey of hallucination in natural language generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/). ACM Comput. Surv., 55(12):248:1–248:38.
68. Mingjian Jiang, Yangjun Ruan, Sicong Huang, Saifei Liao, Silviu Pitis, Roger Baker Grosse, and Jimmy Ba. 2023a. Calibrating language models via augmented prompt ensembles. [find](https://scholar.google.com/scholar?q=Mingjian+Jiang%2C+Yangjun+Ruan%2C+Sicong+Huang%2C+Saifei+Liao%2C+Silviu+Pitis%2C+Roger+Baker+Grosse%2C+and+Jimmy+Ba%2E+2023a%2E+Calibrating+language+models+via+augmented+prompt+ensembles%2E)
69. ICML 2023 Workshop DeployableGenerativeAI. [find](https://scholar.google.com/scholar?q=ICML+2023+Workshop+DeployableGenerativeAI%2E)
70. Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022. Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221. [find](https://scholar.google.com/scholar?q=Saurav+Kadavath%2C+Tom+Conerly%2C+Amanda+Askell%2C+Tom+Henighan%2C+Dawn+Drain%2C+Ethan+Perez%2C+Nicholas+Schiefer%2C+Zac+Hatfield%2DDodds%2C+Nova+DasSarma%2C+Eli+Tran%2DJohnson%2C+et+al%2E+2022%2E+Language+models+%28mostly%29+know+w)
71. Jushi Kai, Tianhang Zhang, Hai Hu, and Zhouhan Lin. 2024. SH2: self-highlighted hesitation helps you decode more truthfully. In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024, pages 4514–4530. Association for Computational Linguistics… [find](https://scholar.google.com/scholar?q=Jushi+Kai%2C+Tianhang+Zhang%2C+Hai+Hu%2C+and+Zhouhan+Lin%2E+2024%2E+SH2%3A+self%2Dhighlighted+hesitation+helps+you+decode+more+truthfully%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2024%2C+M)
72. Haoqiang Kang and Xiao-Yang Liu. 2024. Deficiency of large language models in finance: An empirical examination of hallucination. In I Can’t Believe It’s Not Better Workshop: Failure Modes in the Age of Foundation Models. [find](https://scholar.google.com/scholar?q=Haoqiang+Kang+and+Xiao%2DYang+Liu%2E+2024%2E+Deficiency+of+large+language+models+in+finance%3A+An+empirical+examination+of+hallucination%2E+In+I+Can%E2%80%99t+Believe+It%E2%80%99s+Not+Better+Workshop%3A+Failure+Modes+in+the+Age+)
73. Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, and Kentaro Inui. 2023. [Realtime QA: what’s the answer right now?](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/) In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Proces…
74. Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024. [Bridging the preference gap between retrievers and LLMs](https://md.research.elamur.ai/free/en/papers/ke-2024-bridging-preference-gap-between-retrievers/).
75. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10438–10451, Bangkok, Thailand. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Papers%29%2C+pages+10438%E2%80%9310451%2C+Bangkok%2C+Thailand%2E+Association+for+Computational+Linguistics%2E)
76. Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, and Taeuk Kim. 2024a. Aligning language models to explicitly handle ambiguity. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1989–2007, Miami, Flori… [find](https://scholar.google.com/scholar?q=Hyuhng+Joon+Kim%2C+Youna+Kim%2C+Cheonbok+Park%2C+Junyeob+Kim%2C+Choonghyun+Park%2C+Kang+Min+Yoo%2C+Sang%2Dgoo+Lee%2C+and+Taeuk+Kim%2E+2024a%2E+Aligning+language+models+to+explicitly+handle+ambiguity%2E+In+Proceedings+of+th)
77. Youna Kim, Hyuhng Joon Kim, Cheonbok Park, Choonghyun Park, Hyunsoo Cho, Junyeob Kim, Kang Min Yoo, Sang-goo Lee, and Taeuk Kim. 2024b. Adaptive contrastive decoding in retrieval-augmented generation for handling noisy contexts. In Findings of the Association for Computational Linguistics: EMNLP 202… [find](https://scholar.google.com/scholar?q=Youna+Kim%2C+Hyuhng+Joon+Kim%2C+Cheonbok+Park%2C+Choonghyun+Park%2C+Hyunsoo+Cho%2C+Junyeob+Kim%2C+Kang+Min+Yoo%2C+Sang%2Dgoo+Lee%2C+and+Taeuk+Kim%2E+2024b%2E+Adaptive+contrastive+decoding+in+retrieval%2Daugmented+generation+)
78. Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth Malik, and Yarin Gal. 2024. Semantic entropy probes: Robust and cheap hallucination detection in llms. arXiv preprint arXiv:2406.15927. — [arXiv:2406.15927](https://arxiv.org/abs/2406.15927) [find](https://scholar.google.com/scholar?q=Jannik+Kossen%2C+Jiatong+Han%2C+Muhammed+Razzak%2C+Lisa+Schut%2C+Shreshth+Malik%2C+and+Yarin+Gal%2E+2024%2E+Semantic+entropy+probes%3A+Robust+and+cheap+hallucination+detection+in+llms%2E+arXiv+preprint+arXiv%3A2406%2E15927)
79. Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2022. Clam: Selective clarification for ambiguous questions with large language models. arXiv preprint arXiv:2212.07769. [find](https://scholar.google.com/scholar?q=Lorenz+Kuhn%2C+Yarin+Gal%2C+and+Sebastian+Farquhar%2E+2022%2E+Clam%3A+Selective+clarification+for+ambiguous+questions+with+large+language+models%2E+arXiv+preprint+arXiv%3A2212%2E07769%2E)
80. Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. In The Eleventh International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Lorenz+Kuhn%2C+Yarin+Gal%2C+and+Sebastian+Farquhar%2E+2023%2E+Semantic+uncertainty%3A+Linguistic+invariances+for+uncertainty+estimation+in+natural+language+generation%2E+In+The+Eleventh+International+Conference+o)
81. Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David R. Bellamy, Ramesh Raskar, and Andrew Beam. 2023. Conformal prediction with large language models for multi-choice question answering. CoRR, abs/2305.18404. — [arXiv:2305.18404](https://arxiv.org/abs/2305.18404) [find](https://scholar.google.com/scholar?q=Bhawesh+Kumar%2C+Charlie+Lu%2C+Gauri+Gupta%2C+Anil+Palepu%2C+David+R%2E+Bellamy%2C+Ramesh+Raskar%2C+and+Andrew+Beam%2E+2023%2E+Conformal+prediction+with+large+language+models+for+multi%2Dchoice+question+answering%2E+CoRR%2C+)
82. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019. [Natural questions: a benchmark for question answering research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/). Transactions of the Association for Computational Linguistic…
83. James Law. 2006. Review of "algorithmic learning in a random world by vovk, gammerman and shafer", springer, 2005, ISBN: 0-387-00152-2. SIGACT News, 37(4):38–40. [find](https://scholar.google.com/scholar?q=James+Law%2E+2006%2E+Review+of+%22algorithmic+learning+in+a+random+world+by+vovk%2C+gammerman+and+shafer%22%2C+springer%2C+2005%2C+ISBN%3A+0%2D387%2D00152%2D2%2E+SIGACT+News%2C+37%284%29%3A38%E2%80%9340%2E)
84. Alina Leippert, Tatiana Anikina, Bernd Kiefer, and Josef Genabith. 2024. To clarify or not to clarify: A comparative analysis of clarification classification with fine-tuning, prompt tuning, and prompt engineering. Proceedings of the 2024 Conference of the North American Chapter of the Association f… [find](https://scholar.google.com/scholar?q=Alina+Leippert%2C+Tatiana+Anikina%2C+Bernd+Kiefer%2C+and+Josef+Genabith%2E+2024%2E+To+clarify+or+not+to+clarify%3A+A+comparative+analysis+of+clarification+classification+with+fine%2Dtuning%2C+prompt+tuning%2C+and+promp)
85. Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. 2023a. [Inference-time intervention: Eliciting truthful answers from a language model](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/). In Thirty-seventh Conference on Neural Information Processing Systems.
86. Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. 2024a. [Inference-time intervention: Eliciting truthful answers from a language model](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/). Advances in Neural Information Processing Systems, 36.
87. Loka Li, Guangyi Chen, Yusheng Su, Zhenhao Chen, Yixuan Zhang, Eric Xing, and Kun Zhang. 2024b. Confidence matters: Revisiting intrinsic self-correction capabilities of large language models. arXiv preprint arXiv:2402.12563. — [arXiv:2402.12563](https://arxiv.org/abs/2402.12563) [find](https://scholar.google.com/scholar?q=Loka+Li%2C+Guangyi+Chen%2C+Yusheng+Su%2C+Zhenhao+Chen%2C+Yixuan+Zhang%2C+Eric+Xing%2C+and+Kun+Zhang%2E+2024b%2E+Confidence+matters%3A+Revisiting+intrinsic+self%2Dcorrection+capabilities+of+large+language+models%2E+arXiv+pr)
88. Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, and Tat-Seng Chua. 2023b. Robust prompt optimization for large language models against distribution shifts. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1539–1554. [find](https://scholar.google.com/scholar?q=Moxin+Li%2C+Wenjie+Wang%2C+Fuli+Feng%2C+Yixin+Cao%2C+Jizhi+Zhang%2C+and+Tat%2DSeng+Chua%2E+2023b%2E+Robust+prompt+optimization+for+large+language+models+against+distribution+shifts%2E+In+Proceedings+of+the+2023+Confere)
89. Moxin Li, Wenjie Wang, Fuli Feng, Fengbin Zhu, Qifan Wang, and Tat-Seng Chua. 2024c. Think twice before trusting: Self-detection for large language models through comprehensive answer reflection. In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November… [find](https://scholar.google.com/scholar?q=Moxin+Li%2C+Wenjie+Wang%2C+Fuli+Feng%2C+Fengbin+Zhu%2C+Qifan+Wang%2C+and+Tat%2DSeng+Chua%2E+2024c%2E+Think+twice+before+trusting%3A+Self%2Ddetection+for+large+language+models+through+comprehensive+answer+reflection%2E+In+F)
90. Shuo Li, Sangdon Park, Insup Lee, and Osbert Bastani. 2024d. TRAQ: trustworthy retrieval augmented question answering via conformal prediction. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1:… [find](https://scholar.google.com/scholar?q=Shuo+Li%2C+Sangdon+Park%2C+Insup+Lee%2C+and+Osbert+Bastani%2E+2024d%2E+TRAQ%3A+trustworthy+retrieval+augmented+question+answering+via+conformal+prediction%2E+In+Proceedings+of+the+2024+Conference+of+the+North+Ameri)
91. Siheng Li, Cheng Yang, Taiqiang Wu, Chufan Shi, Yuji Zhang, Xinyu Zhu, Zesen Cheng, Deng Cai, Mo Yu, Lemao Liu, Jie Zhou, Yujiu Yang, Ngai Wong, Xixin Wu, and Wai Lam. 2024e. A survey on the honesty of large language models. arXiv preprint arXiv:2409.18786. — [arXiv:2409.18786](https://arxiv.org/abs/2409.18786) [find](https://scholar.google.com/scholar?q=Siheng+Li%2C+Cheng+Yang%2C+Taiqiang+Wu%2C+Chufan+Shi%2C+Yuji+Zhang%2C+Xinyu+Zhu%2C+Zesen+Cheng%2C+Deng+Cai%2C+Mo+Yu%2C+Lemao+Liu%2C+Jie+Zhou%2C+Yujiu+Yang%2C+Ngai+Wong%2C+Xixin+Wu%2C+and+Wai+Lam%2E+2024e%2E+A+survey+on+the+honesty+o)
92. Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori B Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2023c. [Contrastive decoding: Open-ended text generation as optimization](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1…
93. Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, and Xipeng Qiu. 2023d. [Unified demonstration retriever for in-context learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4644– 46…
94. Xiaonan Li and Xipeng Qiu. 2023. [Finding support examples for in-context learning](https://md.research.elamur.ai/free/en/papers/li-2023-finding-support-examples-context-learning/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6219–6235.
95. Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria, and Lidong Bing. 2024f. Chain-of-knowledge: Grounding large language models via dynamic knowledge adapting over heterogeneous sources. In The Twelfth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Xingxuan+Li%2C+Ruochen+Zhao%2C+Yew+Ken+Chia%2C+Bosheng+Ding%2C+Shafiq+Joty%2C+Soujanya+Poria%2C+and+Lidong+Bing%2E+2024f%2E+Chain%2Dof%2Dknowledge%3A+Grounding+large+language+models+via+dynamic+knowledge+adapting+over+hete)
96. Yinghui Li, Haojing Huang, Jiayi Kuang, Yangning Li, Shu-Yu Guo, Chao Qu, Xiaoyu Tan, Hai-Tao Zheng, Ying Shen, and Philip S. Yu. 2025. Refine knowledge of large language models via adaptive contrastive learning. In The Thirteenth International Conference on Learning Representations, ICLR 2025. [find](https://scholar.google.com/scholar?q=Yinghui+Li%2C+Haojing+Huang%2C+Jiayi+Kuang%2C+Yangning+Li%2C+Shu%2DYu+Guo%2C+Chao+Qu%2C+Xiaoyu+Tan%2C+Hai%2DTao+Zheng%2C+Ying+Shen%2C+and+Philip+S%2E+Yu%2E+2025%2E+Refine+knowledge+of+large+language+models+via+adaptive+contrasti)
97. Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Scott Yih, and Xilun Chen. 2024a. FLAME : Factuality-aware alignment for large language models. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Van… [find](https://scholar.google.com/scholar?q=Sheng%2DChieh+Lin%2C+Luyu+Gao%2C+Barlas+Oguz%2C+Wenhan+Xiong%2C+Jimmy+Lin%2C+Scott+Yih%2C+and+Xilun+Chen%2E+2024a%2E+FLAME+%3A+Factuality%2Daware+alignment+for+large+language+models%2E+In+Advances+in+Neural+Information+Proce)
98. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022a. Teaching models to express their uncertainty in words. [find](https://scholar.google.com/scholar?q=Stephanie+Lin%2C+Jacob+Hilton%2C+and+Owain+Evans%2E+2022a%2E+Teaching+models+to+express+their+uncertainty+in+words%2E)
99. Trans. Mach. Learn. Res., 2022. [find](https://scholar.google.com/scholar?q=Trans%2E+Mach%2E+Learn%2E+Res%2E%2C+2022%2E)
100. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022b. [Truthfulqa: Measuring how models mimic human falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252.
101. Zhen Lin, Shubhendu Trivedi, and Jimeng Sun. 2024b. Generating with confidence: Uncertainty quantification for black-box large language models. Transactions on Machine Learning Research. [find](https://scholar.google.com/scholar?q=Zhen+Lin%2C+Shubhendu+Trivedi%2C+and+Jimeng+Sun%2E+2024b%2E+Generating+with+confidence%3A+Uncertainty+quantification+for+black%2Dbox+large+language+models%2E+Transactions+on+Machine+Learning+Research%2E)
102. Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, et al. 2024. Uncertainty quantification for in-context learning of large language models. In Proceedings of the 2024 Conference of the North American Chapter of the Association… [find](https://scholar.google.com/scholar?q=Chen+Ling%2C+Xujiang+Zhao%2C+Xuchao+Zhang%2C+Wei+Cheng%2C+Yanchi+Liu%2C+Yiyou+Sun%2C+Mika+Oishi%2C+Takao+Osaki%2C+Katsushi+Matsuda%2C+Jie+Ji%2C+et+al%2E+2024%2E+Uncertainty+quantification+for+in%2Dcontext+learning+of+large+lan)
103. Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi. 2023. [We’re afraid language models aren’t modeling ambiguity](https://md.research.elamur.ai/free/en/papers/liu-2023-we-re-afraid-language-models/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Si…
104. Genglin Liu, Xingyao Wang, Lifan Yuan, Yangyi Chen, and Hao Peng. 2024a. Examining llms’ uncertainty expression towards questions outside parametric knowledge. Preprint, arXiv:2311.09731. — [arXiv:2311.09731](https://arxiv.org/abs/2311.09731) [find](https://scholar.google.com/scholar?q=Genglin+Liu%2C+Xingyao+Wang%2C+Lifan+Yuan%2C+Yangyi+Chen%2C+and+Hao+Peng%2E+2024a%2E+Examining+llms%E2%80%99+uncertainty+expression+towards+questions+outside+parametric+knowledge%2E+Preprint%2C+arXiv%3A2311%2E09731%2E+%E2%80%94+%5BarXiv%3A231)
105. Jiachang Liu, Dinghan Shen, Yizhe Zhang, William B Dolan, Lawrence Carin, and Weizhu Chen. 2022a. [What makes good in-context examples for gpt-3?](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/) In Proceedings of Deep Learning Inside Out (Dee-LIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages…
106. Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi. 2022b. Generated knowledge prompting for commonsense reasoning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages… [find](https://scholar.google.com/scholar?q=Jiacheng+Liu%2C+Alisa+Liu%2C+Ximing+Lu%2C+Sean+Welleck%2C+Peter+West%2C+Ronan+Le+Bras%2C+Yejin+Choi%2C+and+Hannaneh+Hajishirzi%2E+2022b%2E+Generated+knowledge+prompting+for+commonsense+reasoning%2E+In+Proceedings+of+the+)
107. Junteng Liu, Shiqi Chen, Yu Cheng, and Junxian He. 2024b. On the universal truthfulness hyperplane inside llms. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024, pages 18199–18224. Association for Computationa… [find](https://scholar.google.com/scholar?q=Junteng+Liu%2C+Shiqi+Chen%2C+Yu+Cheng%2C+and+Junxian+He%2E+2024b%2E+On+the+universal+truthfulness+hyperplane+inside+llms%2E+In+Proceedings+of+the+2024+Conference+on+Empirical+Methods+in+Natural+Language+Processin)
108. Kai Liu, Ze Chen, Zhihang Fu, Wei Zhang, Rongxin Jiang, Fan Zhou, Yaowu Chen, Yue Wu, and Jieping Ye. 2025. Structure-aware domain knowledge injection for large language models. Preprint, arXiv:2407.16724. [find](https://scholar.google.com/scholar?q=Kai+Liu%2C+Ze+Chen%2C+Zhihang+Fu%2C+Wei+Zhang%2C+Rongxin+Jiang%2C+Fan+Zhou%2C+Yaowu+Chen%2C+Yue+Wu%2C+and+Jieping+Ye%2E+2025%2E+Structure%2Daware+domain+knowledge+injection+for+large+language+models%2E+Preprint%2C+arXiv%3A2407%2E1)
109. Xin Liu, Muhammad Khalifa, and Lu Wang. 2024c. Litcab: Lightweight language model calibration over short-and long-form responses. In The Twelfth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Xin+Liu%2C+Muhammad+Khalifa%2C+and+Lu+Wang%2E+2024c%2E+Litcab%3A+Lightweight+language+model+calibration+over+short%2Dand+long%2Dform+responses%2E+In+The+Twelfth+International+Conference+on+Learning+Representations%2E)
110. Do Xuan Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Xu Zhao, Nancy F. Chen, Kenji Kawaguchi, Michael Shieh, and Junxian He. 2024. Prompt optimization via adversarial in-context learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa… [find](https://scholar.google.com/scholar?q=Do+Xuan+Long%2C+Yiran+Zhao%2C+Hannah+Brown%2C+Yuxi+Xie%2C+James+Xu+Zhao%2C+Nancy+F%2E+Chen%2C+Kenji+Kawaguchi%2C+Michael+Shieh%2C+and+Junxian+He%2E+2024%2E+Prompt+optimization+via+adversarial+in%2Dcontext+learning%2E+In+Procee)
111. Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022. [Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity](https://md.research.elamur.ai/free/en/papers/lu-2022-fantastically-ordered-prompts-where-find/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), A…
112. Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, and Vincent Y Zhao. 2023. Dr.ICL: Demonstration-retrieved in-context learning. In R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models. [find](https://scholar.google.com/scholar?q=Man+Luo%2C+Xin+Xu%2C+Zhuyun+Dai%2C+Panupong+Pasupat%2C+Mehran+Kazemi%2C+Chitta+Baral%2C+Vaiva+Imbrasaite%2C+and+Vincent+Y+Zhao%2E+2023%2E+Dr%2EICL%3A+Demonstration%2Dretrieved+in%2Dcontext+learning%2E+In+R0%2DFoMo%3ARobustness+of+Fe)
113. Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023. [Query rewriting in retrieval-augmented large language models](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5303–5315, Singapore. Association for Computational Linguistics.
114. Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang. 2022. Memory-assisted prompt editing to improve GPT-3 after deployment. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pages 2833–286… [find](https://scholar.google.com/scholar?q=Aman+Madaan%2C+Niket+Tandon%2C+Peter+Clark%2C+and+Yiming+Yang%2E+2022%2E+Memory%2Dassisted+prompt+editing+to+improve+GPT%2D3+after+deployment%2E+In+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+L)
115. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2024. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems, 36. [find](https://scholar.google.com/scholar?q=Aman+Madaan%2C+Niket+Tandon%2C+Prakhar+Gupta%2C+Skyler+Hallinan%2C+Luyu+Gao%2C+Sarah+Wiegreffe%2C+Uri+Alon%2C+Nouha+Dziri%2C+Shrimai+Prabhumoye%2C+Yiming+Yang%2C+et+al%2E+2024%2E+Self%2Drefine%3A+Iterative+refinement+with+self%2Df)
116. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2022. When not to trust language models: Investigating effectiveness of parametric and non-parametric memories. arXiv preprint arXiv:2212.10511. [find](https://scholar.google.com/scholar?q=Alex+Mallen%2C+Akari+Asai%2C+Victor+Zhong%2C+Rajarshi+Das%2C+Daniel+Khashabi%2C+and+Hannaneh+Hajishirzi%2E+2022%2E+When+not+to+trust+language+models%3A+Investigating+effectiveness+of+parametric+and+non%2Dparametric+mem)
117. Potsawee Manakul, Adian Liusie, and Mark Gales. 2023. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9004–9017. [find](https://scholar.google.com/scholar?q=Potsawee+Manakul%2C+Adian+Liusie%2C+and+Mark+Gales%2E+2023%2E+Selfcheckgpt%3A+Zero%2Dresource+black%2Dbox+hallucination+detection+for+generative+large+language+models%2E+In+Proceedings+of+the+2023+Conference+on+Empir)
118. Samuel Marks and Max Tegmark. 2023. [The geometry of truth: Emergent linear structure in large language model representations of true/false datasets](https://md.research.elamur.ai/free/en/papers/marks-2023-geometry-truth-emergent-linear-structure/). arXiv preprint arXiv:2310.06824.
119. Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, and George Karypis. 2023. Which examples to annotate for in-context learning? towards effective and efficient selection. arXiv preprint arXiv:2310.20046. — [arXiv:2310.20046](https://arxiv.org/abs/2310.20046) [find](https://scholar.google.com/scholar?q=Costas+Mavromatis%2C+Balasubramaniam+Srinivasan%2C+Zhengyuan+Shen%2C+Jiani+Zhang%2C+Huzefa+Rangwala%2C+Christos+Faloutsos%2C+and+George+Karypis%2E+2023%2E+Which+examples+to+annotate+for+in%2Dcontext+learning%3F+towards+e)
120. Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Leonardo Barros Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, Tolga Aktas, and Todd Hendry. 2024. Injecting new knowledge into large language models via supervised fine-tuning. ArXiv, abs/24… — [arXiv:2404.00213](https://arxiv.org/abs/2404.00213) [find](https://scholar.google.com/scholar?q=Nick+Mecklenburg%2C+Yiyou+Lin%2C+Xiaoxiao+Li%2C+Daniel+Holstein%2C+Leonardo+Nunes%2C+Sara+Malvar%2C+Bruno+Leonardo+Barros+Silva%2C+Ranveer+Chandra%2C+Vijay+Aski%2C+Pavan+Kumar+Reddy+Yannam%2C+Tolga+Aktas%2C+and+Todd+Hendry)
121. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. Locating and editing factual associations in GPT. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022. [find](https://scholar.google.com/scholar?q=Kevin+Meng%2C+David+Bau%2C+Alex+Andonian%2C+and+Yonatan+Belinkov%2E+2022%2E+Locating+and+editing+factual+associations+in+GPT%2E+In+Advances+in+Neural+Information+Processing+Systems+35%3A+Annual+Conference+on+Neural)
122. Kevin Meng, Arnab Sen Sharma, Alex J. Andonian, Yonatan Belinkov, and David Bau. 2023. Mass-editing memory in a transformer. In The Eleventh International Conference on Learning Representations, ICLR 2023. [find](https://scholar.google.com/scholar?q=Kevin+Meng%2C+Arnab+Sen+Sharma%2C+Alex+J%2E+Andonian%2C+Yonatan+Belinkov%2C+and+David+Bau%2E+2023%2E+Mass%2Dediting+memory+in+a+transformer%2E+In+The+Eleventh+International+Conference+on+Learning+Representations%2C+ICLR+)
123. Ning Miao, Yee Whye Teh, and Tom Rainforth. 2024. Selfcheck: Using LLMs to zero-shot check their own step-by-step reasoning. In The Twelfth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Ning+Miao%2C+Yee+Whye+Teh%2C+and+Tom+Rainforth%2E+2024%2E+Selfcheck%3A+Using+LLMs+to+zero%2Dshot+check+their+own+step%2Dby%2Dstep+reasoning%2E+In+The+Twelfth+International+Conference+on+Learning+Representations%2E)
124. Sabrina J Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau. 2022. Reducing conversational agents’ overconfidence through linguistic calibration. Transactions of the Association for Computational Linguistics, 10:857–872. [find](https://scholar.google.com/scholar?q=Sabrina+J+Mielke%2C+Arthur+Szlam%2C+Emily+Dinan%2C+and+Y%2DLan+Boureau%2E+2022%2E+Reducing+conversational+agents%E2%80%99+overconfidence+through+linguistic+calibration%2E+Transactions+of+the+Association+for+Computational+L)
125. Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023. [Factscore: Fine-grained atomic evaluation of factual precision in long form text generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/). ArXiv, abs/2305.14251.
126. Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, and Chelsea Finn. 2022. Memory-based model editing at scale. In International Conference on Machine Learning, ICML 2022, volume 162 of Proceedings of Machine Learning Research, pages 15817–15831. [find](https://scholar.google.com/scholar?q=Eric+Mitchell%2C+Charles+Lin%2C+Antoine+Bosselut%2C+Christopher+D%2E+Manning%2C+and+Chelsea+Finn%2E+2022%2E+Memory%2Dbased+model+editing+at+scale%2E+In+International+Conference+on+Machine+Learning%2C+ICML+2022%2C+volume+16)
127. Christopher Mohri and Tatsunori Hashimoto. 2024. Language models with conformal factuality guarantees. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net. [find](https://scholar.google.com/scholar?q=Christopher+Mohri+and+Tatsunori+Hashimoto%2E+2024%2E+Language+models+with+conformal+factuality+guarantees%2E+In+Forty%2Dfirst+International+Conference+on+Machine+Learning%2C+ICML+2024%2C+Vienna%2C+Austria%2C+July+21%2D)
128. Fangwen Mu, Lin Shi, Song Wang, Zhuohao Yu, Binquan Zhang, ChenXue Wang, Shichao Liu, and Qing Wang. 2023. Clarifygpt: Empowering llm-based code generation with intention clarification. ArXiv, abs/2310.10996. — [arXiv:2310.10996](https://arxiv.org/abs/2310.10996) [find](https://scholar.google.com/scholar?q=Fangwen+Mu%2C+Lin+Shi%2C+Song+Wang%2C+Zhuohao+Yu%2C+Binquan+Zhang%2C+ChenXue+Wang%2C+Shichao+Liu%2C+and+Qing+Wang%2E+2023%2E+Clarifygpt%3A+Empowering+llm%2Dbased+code+generation+with+intention+clarification%2E+ArXiv%2C+abs%2F231)
129. Tarek Naous, Michael J. Ryan, Alan Ritter, and Wei Xu. 2024. [Having beer after prayer? measuring cultural bias in large language models](https://md.research.elamur.ai/free/en/papers/naous-2023-having-beer-after-prayer-measuring/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024…
130. Alexander Nikitin, Jannik Kossen, Yarin Gal, and Pekka Marttinen. 2024. Kernel language entropy: Fine-grained uncertainty quantification for llms from semantic similarities. arXiv preprint arXiv:2405.20003. — [arXiv:2405.20003](https://arxiv.org/abs/2405.20003) [find](https://scholar.google.com/scholar?q=Alexander+Nikitin%2C+Jannik+Kossen%2C+Yarin+Gal%2C+and+Pekka+Marttinen%2E+2024%2E+Kernel+language+entropy%3A+Fine%2Dgrained+uncertainty+quantification+for+llms+from+semantic+similarities%2E+arXiv+preprint+arXiv%3A2405%2E)
131. Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi, and Greg Durrett. 2022. [Entity cloze by date: What lms know about unseen entities](https://md.research.elamur.ai/free/en/papers/onoe-2022-entity-cloze-date-what-lms/). In Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, WA, United States, July 10-15, 2022, pages 693–702. Association for Computational Lin…
132. Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2023. [Med-halt: Medical domain hallucination test for large language models](https://md.research.elamur.ai/free/en/papers/pal-2023-med-halt-medical-domain-hallucination/). In Proceedings of the 27th Conference on Computational Natural Language Learning, CoNLL 2023, Singapore, December 6-7, 2023, pages 314–334. Association for Com…
133. Soumen Pal, Manojit Bhattacharya, Sang-Soo Lee, and Chiranjib Chakraborty. 2024. A domain-specific next-generation large language model (llm) or chatgpt is required for biomedical engineering and research. Annals of biomedical engineering, 52(3):451– 454. [find](https://scholar.google.com/scholar?q=Soumen+Pal%2C+Manojit+Bhattacharya%2C+Sang%2DSoo+Lee%2C+and+Chiranjib+Chakraborty%2E+2024%2E+A+domain%2Dspecific+next%2Dgeneration+large+language+model+%28llm%29+or+chatgpt+is+required+for+biomedical+engineering+and+rese)
134. Yikang Pan, Liangming Pan, Wenhu Chen, Preslav Nakov, Min-Yen Kan, and William Yang Wang. 2023. [On the risk of misinformation pollution with large language models](https://md.research.elamur.ai/free/en/papers/pan-2023-risk-misinformation-pollution-large-language/). In Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023, pages 1389–1403. Association f…
135. Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. 2023. [Grips: Gradient-free, edit-based instruction search for prompting large language models](https://md.research.elamur.ai/free/en/papers/prasad-2023-grips-gradient-free-edit-based/). In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3845–3864.
136. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. 2023. Measuring and narrowing the compositionality gap in language models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5687–5711. [find](https://scholar.google.com/scholar?q=Ofir+Press%2C+Muru+Zhang%2C+Sewon+Min%2C+Ludwig+Schmidt%2C+Noah+A+Smith%2C+and+Mike+Lewis%2E+2023%2E+Measuring+and+narrowing+the+compositionality+gap+in+language+models%2E+In+Findings+of+the+Association+for+Computati)
137. Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu, and Michael Zeng. 2023. [Automatic prompt optimization with “gradient descent” and beam search](https://md.research.elamur.ai/free/en/papers/pryzant-2023-automatic-prompt-optimization-gradient-descent/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7957–7968.
138. Yifu Qiu, Zheng Zhao, Yftah Ziser, Anna Korhonen, Edoardo M Ponti, and Shay B Cohen. 2024. Spectral editing of activations for large language model alignment. arXiv preprint arXiv:2405.09719. — [arXiv:2405.09719](https://arxiv.org/abs/2405.09719) [find](https://scholar.google.com/scholar?q=Yifu+Qiu%2C+Zheng+Zhao%2C+Yftah+Ziser%2C+Anna+Korhonen%2C+Edoardo+M+Ponti%2C+and+Shay+B+Cohen%2E+2024%2E+Spectral+editing+of+activations+for+large+language+model+alignment%2E+arXiv+preprint+arXiv%3A2405%2E09719%2E+%E2%80%94+%5BarXiv)
139. Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, and Regina Barzilay. 2024. Conformal language modeling. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net. [find](https://scholar.google.com/scholar?q=Victor+Quach%2C+Adam+Fisch%2C+Tal+Schuster%2C+Adam+Yala%2C+Jae+Ho+Sohn%2C+Tommi+S%2E+Jaakkola%2C+and+Regina+Barzilay%2E+2024%2E+Conformal+language+modeling%2E+In+The+Twelfth+International+Conference+on+Learning+Represent)
140. Shauli Ravfogel, Yoav Goldberg, and Jacob Goldberger. 2023. Conformal nucleus sampling. In Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023, pages 27–34. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Shauli+Ravfogel%2C+Yoav+Goldberg%2C+and+Jacob+Goldberger%2E+2023%2E+Conformal+nucleus+sampling%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2023%2C+Toronto%2C+Canada%2C+July+9%2D14%2C+2023%2C+pages+)
141. Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2023. [Investigating the factual knowledge boundary of large language models with retrieval augmentation](https://md.research.elamur.ai/free/en/papers/ren-2025-investigating-factual-knowledge-boundary-large/). arXiv preprint arXiv:2307.11019.
142. Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022. [Learning to retrieve prompts for in-context learning](https://md.research.elamur.ai/free/en/papers/rubin-2021-learning-retrieve-prompts-context-learning/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2655–2671.
143. Abulhair Saparov and He He. 2023. [Language models are greedy reasoners: A systematic formal analysis of chain-of-thought](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/). In The Eleventh International Conference on Learning Representations.
144. Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. 2023. [Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9248–9274, Singapore. Association for…
145. Maohao Shen, Subhro Das, Kristjan Greenewald, Prasanna Sattigeri, Gregory Wornell, and Soumya Ghosh. 2024. Thermometer: Towards universal calibration for large language models. arXiv preprint arXiv:2403.08819. — [arXiv:2403.08819](https://arxiv.org/abs/2403.08819) [find](https://scholar.google.com/scholar?q=Maohao+Shen%2C+Subhro+Das%2C+Kristjan+Greenewald%2C+Prasanna+Sattigeri%2C+Gregory+Wornell%2C+and+Soumya+Ghosh%2E+2024%2E+Thermometer%3A+Towards+universal+calibration+for+large+language+models%2E+arXiv+preprint+arXiv%3A24)
146. Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023. [Large language models can be easily distracted by irrelevant context](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/). In International Conference on Machine Learning, pages 31210–31227. PMLR.
147. Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Richard James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2024. [REPLUG: Retrieval-augmented black-box language models](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Hum…
148. Vaishnavi Shrivastava, Percy Liang, and Ananya Kumar. 2024. Llamas know what GPTs don’t show: Surrogate models for selective classification. [find](https://scholar.google.com/scholar?q=Vaishnavi+Shrivastava%2C+Percy+Liang%2C+and+Ananya+Kumar%2E+2024%2E+Llamas+know+what+GPTs+don%E2%80%99t+show%3A+Surrogate+models+for+selective+classification%2E)
149. Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Lee Boyd-Graber, and Lijuan Wang. 2023. [Prompting GPT-3 to be reliable](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/). In The Eleventh International Conference on Learning Representations.
150. Smriti Singh, Shuvam Keshari, Vinija Jain, and Aman Chadha. 2024. Born with a silver spoon? investigating socioeconomic bias in large language models. ArXiv, abs/2403.14633. — [arXiv:2403.14633](https://arxiv.org/abs/2403.14633) [find](https://scholar.google.com/scholar?q=Smriti+Singh%2C+Shuvam+Keshari%2C+Vinija+Jain%2C+and+Aman+Chadha%2E+2024%2E+Born+with+a+silver+spoon%3F+investigating+socioeconomic+bias+in+large+language+models%2E+ArXiv%2C+abs%2F2403%2E14633%2E+%E2%80%94+%5BarXiv%3A2403%2E14633%5D%28https)
151. Maojia Song, Shang Hong Sim, Rishabh Bhardwaj, Hai Leong Chieu, Navonil Majumder, and Soujanya Poria. 2024a. Measuring and enhancing trustworthiness of llms in rag through grounded attributions and learning to refuse. ArXiv, abs/2409.11242. — [arXiv:2409.11242](https://arxiv.org/abs/2409.11242) [find](https://scholar.google.com/scholar?q=Maojia+Song%2C+Shang+Hong+Sim%2C+Rishabh+Bhardwaj%2C+Hai+Leong+Chieu%2C+Navonil+Majumder%2C+and+Soujanya+Poria%2E+2024a%2E+Measuring+and+enhancing+trustworthiness+of+llms+in+rag+through+grounded+attributions+and+le)
152. Xiaoshuai Song, Zhengyang Wang, Keqing He, Guanting Dong, Yutao Mou, Jinxu Zhao, and Weiran Xu. 2024b. Knowledge editing on black-box large language models. CoRR, abs/2402.08631. — [arXiv:2402.08631](https://arxiv.org/abs/2402.08631) [find](https://scholar.google.com/scholar?q=Xiaoshuai+Song%2C+Zhengyang+Wang%2C+Keqing+He%2C+Guanting+Dong%2C+Yutao+Mou%2C+Jinxu+Zhao%2C+and+Weiran+Xu%2E+2024b%2E+Knowledge+editing+on+black%2Dbox+large+language+models%2E+CoRR%2C+abs%2F2402%2E08631%2E+%E2%80%94+%5BarXiv%3A2402%2E08631%5D%28)
153. Elias Stengel-Eskin, Peter Hase, and Mohit Bansal. 2024. Lacie: Listener-aware finetuning for confidence calibration in large language models. arXiv preprint arXiv:2405.21028. [find](https://scholar.google.com/scholar?q=Elias+Stengel%2DEskin%2C+Peter+Hase%2C+and+Mohit+Bansal%2E+2024%2E+Lacie%3A+Listener%2Daware+finetuning+for+confidence+calibration+in+large+language+models%2E+arXiv+preprint+arXiv%3A2405%2E21028%2E)
154. Jiayuan Su, Jing Luo, Hongwei Wang, and Lu Cheng. 2024. API is enough: Conformal prediction for large language models without logit-access. In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024, pages 979–995. Association for Computationa… [find](https://scholar.google.com/scholar?q=Jiayuan+Su%2C+Jing+Luo%2C+Hongwei+Wang%2C+and+Lu+Cheng%2E+2024%2E+API+is+enough%3A+Conformal+prediction+for+large+language+models+without+logit%2Daccess%2E+In+Findings+of+the+Association+for+Computational+Linguistics)
155. Chenmien Tan, Ge Zhang, and Jie Fu. 2024. Massive editing for large language models via meta learning. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net. [find](https://scholar.google.com/scholar?q=Chenmien+Tan%2C+Ge+Zhang%2C+and+Jie+Fu%2E+2024%2E+Massive+editing+for+large+language+models+via+meta+learning%2E+In+The+Twelfth+International+Conference+on+Learning+Representations%2C+ICLR+2024%2C+Vienna%2C+Austria%2C+)
156. Shuchang Tao, Liuyi Yao, Hanxing Ding, Yuexiang Xie, Qi Cao, Fei Sun, Jinyang Gao, Huawei Shen, and Bolin Ding. 2024. When to trust LLMs: Aligning confidence with response quality. In Findings of the Association for Computational Linguistics ACL 2024, pages 5984–5996, Bangkok, Thailand and virtual m… [find](https://scholar.google.com/scholar?q=Shuchang+Tao%2C+Liuyi+Yao%2C+Hanxing+Ding%2C+Yuexiang+Xie%2C+Qi+Cao%2C+Fei+Sun%2C+Jinyang+Gao%2C+Huawei+Shen%2C+and+Bolin+Ding%2E+2024%2E+When+to+trust+LLMs%3A+Aligning+confidence+with+response+quality%2E+In+Findings+of+the+)
157. Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D. Manning, and Chelsea Finn. 2024. [Fine-tuning language models for factuality](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/). In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net.
158. Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. 2023. Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. In Proceedings of the 2023 Confer… [find](https://scholar.google.com/scholar?q=Katherine+Tian%2C+Eric+Mitchell%2C+Allan+Zhou%2C+Archit+Sharma%2C+Rafael+Rafailov%2C+Huaxiu+Yao%2C+Chelsea+Finn%2C+and+Christopher+D+Manning%2E+2023%2E+Just+ask+for+calibration%3A+Strategies+for+eliciting+calibrated+conf)
159. Christian Tomani, Kamalika Chaudhuri, I. Evtimov, Daniel Cremers, and Mark Ibrahim. 2024. Uncertainty-based abstention in llms improves safety and reduces hallucinations. ArXiv, abs/2404.10960. — [arXiv:2404.10960](https://arxiv.org/abs/2404.10960) [find](https://scholar.google.com/scholar?q=Christian+Tomani%2C+Kamalika+Chaudhuri%2C+I%2E+Evtimov%2C+Daniel+Cremers%2C+and+Mark+Ibrahim%2E+2024%2E+Uncertainty%2Dbased+abstention+in+llms+improves+safety+and+reduces+hallucinations%2E+ArXiv%2C+abs%2F2404%2E10960%2E+%E2%80%94+%5BarX)
160. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,… — [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
161. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. MuSiQue: Multi-hop questions via single-hop question composition. Transactions of the Association for Computational Linguistics, 10:539–554. [find](https://scholar.google.com/scholar?q=Harsh+Trivedi%2C+Niranjan+Balasubramanian%2C+Tushar+Khot%2C+and+Ashish+Sabharwal%2E+2022%2E+MuSiQue%3A+Multi%2Dhop+questions+via+single%2Dhop+question+composition%2E+Transactions+of+the+Association+for+Computational+Li)
162. Neeraj Varshney, Pavel Dolin, Agastya Seth, and Chitta Baral. 2023. The art of defending: A systematic evaluation and analysis of llm defense strategies on safety and over-defensiveness. ArXiv, abs/2401.00287. [find](https://scholar.google.com/scholar?q=Neeraj+Varshney%2C+Pavel+Dolin%2C+Agastya+Seth%2C+and+Chitta+Baral%2E+2023%2E+The+art+of+defending%3A+A+systematic+evaluation+and+analysis+of+llm+defense+strategies+on+safety+and+over%2Ddefensiveness%2E+ArXiv%2C+abs%2F24)
163. Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, and Thang Luong. 2023. [Freshllms: Refreshing large language models with search engine augmentation](https://md.research.elamur.ai/free/en/papers/vu-2023-freshllms-refreshing-large-language-models/). In Annual Meeting of the Association for Computational Linguistics.
164. Boshi Wang, Xiang Deng, and Huan Sun. 2022. Iteratively prompt pre-trained language models for chain of thought. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2714–2730. [find](https://scholar.google.com/scholar?q=Boshi+Wang%2C+Xiang+Deng%2C+and+Huan+Sun%2E+2022%2E+Iteratively+prompt+pre%2Dtrained+language+models+for+chain+of+thought%2E+In+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+Language+Processi)
165. Boshi Wang, Xiang Yue, and Huan Sun. 2023a. Can chatgpt defend its belief in truth? evaluating LLM reasoning via debate. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11865–11881. [find](https://scholar.google.com/scholar?q=Boshi+Wang%2C+Xiang+Yue%2C+and+Huan+Sun%2E+2023a%2E+Can+chatgpt+defend+its+belief+in+truth%3F+evaluating+LLM+reasoning+via+debate%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2023%2C+pages)
166. Jianing Wang, Yang Zhou, Xiaocheng Zhang, Mengjiao Bao, and Peng Yan. 2024a. Self-evolutionary large language models through uncertainty-enhanced preference optimization. arXiv preprint arXiv:2409.11212. — [arXiv:2409.11212](https://arxiv.org/abs/2409.11212) [find](https://scholar.google.com/scholar?q=Jianing+Wang%2C+Yang+Zhou%2C+Xiaocheng+Zhang%2C+Mengjiao+Bao%2C+and+Peng+Yan%2E+2024a%2E+Self%2Devolutionary+large+language+models+through+uncertainty%2Denhanced+preference+optimization%2E+arXiv+preprint+arXiv%3A2409%2E112)
167. Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. 2024b. Knowledge mechanisms in large language models: A survey and perspective. In Findings of the Association for Computational Lingui… [find](https://scholar.google.com/scholar?q=Mengru+Wang%2C+Yunzhi+Yao%2C+Ziwen+Xu%2C+Shuofei+Qiao%2C+Shumin+Deng%2C+Peng+Wang%2C+Xiang+Chen%2C+Jia%2DChen+Gu%2C+Yong+Jiang%2C+Pengjun+Xie%2C+Fei+Huang%2C+Huajun+Chen%2C+and+Ningyu+Zhang%2E+2024b%2E+Knowledge+mechanisms+in+larg)
168. Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, et al. 2024c. Knowledge mechanisms in large language models: A survey and perspective. arXiv preprint arXiv:2407.15017. [find](https://scholar.google.com/scholar?q=Mengru+Wang%2C+Yunzhi+Yao%2C+Ziwen+Xu%2C+Shuofei+Qiao%2C+Shumin+Deng%2C+Peng+Wang%2C+Xiang+Chen%2C+Jia%2DChen+Gu%2C+Yong+Jiang%2C+Pengjun+Xie%2C+et+al%2E+2024c%2E+Knowledge+mechanisms+in+large+language+models%3A+A+survey+and+per)
169. Peng Wang, Zexi Li, Ningyu Zhang, Ziwen Xu, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. 2024d. WISE: rethinking the knowledge memory for lifelong model editing of large language models. CoRR, abs/2405.14768. [find](https://scholar.google.com/scholar?q=Peng+Wang%2C+Zexi+Li%2C+Ningyu+Zhang%2C+Ziwen+Xu%2C+Yunzhi+Yao%2C+Yong+Jiang%2C+Pengjun+Xie%2C+Fei+Huang%2C+and+Huajun+Chen%2E+2024d%2E+WISE%3A+rethinking+the+knowledge+memory+for+lifelong+model+editing+of+large+language+m)
170. Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric Xing, and Zhiting Hu. 2024e. Promptagent: Strategic planning with language models enables expert-level prompt optimization. In The Twelfth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Xinyuan+Wang%2C+Chenxi+Li%2C+Zhen+Wang%2C+Fan+Bai%2C+Haotian+Luo%2C+Jiayou+Zhang%2C+Nebojsa+Jojic%2C+Eric+Xing%2C+and+Zhiting+Hu%2E+2024e%2E+Promptagent%3A+Strategic+planning+with+language+models+enables+expert%2Dlevel+promp)
171. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023b. [Self-consistency improves chain of thought reasoning in language models](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/). In The Eleventh International Conference on Learning Representations.
172. Zhen Wang, Peide Zhu, and Jie Yang. 2024f. Controver-sialqa: Exploring controversy in question answering. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy, pages 3962–3966. ELRA… [find](https://scholar.google.com/scholar?q=Zhen+Wang%2C+Peide+Zhu%2C+and+Jie+Yang%2E+2024f%2E+Controver%2Dsialqa%3A+Exploring+controversy+in+question+answering%2E+In+Proceedings+of+the+2024+Joint+International+Conference+on+Computational+Linguistics%2C+Langua)
173. Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, and Graham Neubig. 2023c. [Learning to filter context for retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/wang-2023-learning-filter-context-retrieval-augmented/). arXiv preprint arXiv:2311.08377.
174. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022a. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). In Advances in Neural Information Processing Systems, volume 35, pages 24824–24837. Curran Associates, Inc.
175. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022b. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems, 35:24824–24837.
176. Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, et al. 2024. [Long-form factuality in large language models](https://md.research.elamur.ai/free/en/papers/wei-2024-long-form-factuality-large-language/). arXiv preprint arXiv:2403.18802.
177. Bingbing Wen, Bill Howe, and Lucy Lu Wang. 2024a. Characterizing LLM abstention behavior in science QA with context perturbations. In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024, pages 3437–3450. Association for Computational Lingu… [find](https://scholar.google.com/scholar?q=Bingbing+Wen%2C+Bill+Howe%2C+and+Lucy+Lu+Wang%2E+2024a%2E+Characterizing+LLM+abstention+behavior+in+science+QA+with+context+perturbations%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2)
178. Bingbing Wen, Jihan Yao, Shangbin Feng, Chenjun Xu, Yulia Tsvetkov, Bill Howe, and Lucy Lu Wang. 2024b. Know your limits: A survey of abstention in large language models. arXiv preprint arXiv:2407.18418. [find](https://scholar.google.com/scholar?q=Bingbing+Wen%2C+Jihan+Yao%2C+Shangbin+Feng%2C+Chenjun+Xu%2C+Yulia+Tsvetkov%2C+Bill+Howe%2C+and+Lucy+Lu+Wang%2E+2024b%2E+Know+your+limits%3A+A+survey+of+abstention+in+large+language+models%2E+arXiv+preprint+arXiv%3A2407%2E184)
179. Zhihua Wen, Zhiliang Tian, Zexin Jian, Zhen Huang, Pei Ke, Yifu Gao, Minlie Huang, and Dongsheng Li. 2024c. Perception of knowledge boundary for large language models through semi-open-ended question answering. CoRR, abs/2405.14383. — [arXiv:2405.14383](https://arxiv.org/abs/2405.14383) [find](https://scholar.google.com/scholar?q=Zhihua+Wen%2C+Zhiliang+Tian%2C+Zexin+Jian%2C+Zhen+Huang%2C+Pei+Ke%2C+Yifu+Gao%2C+Minlie+Huang%2C+and+Dongsheng+Li%2E+2024c%2E+Perception+of+knowledge+boundary+for+large+language+models+through+semi%2Dopen%2Dended+question+)
180. Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, and Jun Zhao. 2023. [Large language models are better reasoners with self-verification](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2550–2575.
181. Jian Wu, Linyi Yang, Yuliang Ji, Wenhao Huang, Börje F Karlsson, and Manabu Okumura. 2024a. Gendec: A robust generative question-decomposition method for multi-hop reasoning. arXiv preprint arXiv:2402.11166. — [arXiv:2402.11166](https://arxiv.org/abs/2402.11166) [find](https://scholar.google.com/scholar?q=Jian+Wu%2C+Linyi+Yang%2C+Yuliang+Ji%2C+Wenhao+Huang%2C+B%C3%B6rje+F+Karlsson%2C+and+Manabu+Okumura%2E+2024a%2E+Gendec%3A+A+robust+generative+question%2Ddecomposition+method+for+multi%2Dhop+reasoning%2E+arXiv+preprint+arXiv%3A2402)
182. Siye Wu, Jian Xie, Jiangjie Chen, Tinghui Zhu, Kai Zhang, and Yanghua Xiao. 2024b. How easily do irrelevant inputs skew the responses of large language models? ArXiv, abs/2404.03302. [find](https://scholar.google.com/scholar?q=Siye+Wu%2C+Jian+Xie%2C+Jiangjie+Chen%2C+Tinghui+Zhu%2C+Kai+Zhang%2C+and+Yanghua+Xiao%2E+2024b%2E+How+easily+do+irrelevant+inputs+skew+the+responses+of+large+language+models%3F+ArXiv%2C+abs%2F2404%2E03302%2E)
183. Johnathan Xie, Annie S Chen, Yoonho Lee, Eric Mitchell, and Chelsea Finn. 2024. Calibrating language models with adaptive temperature scaling. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 18128–18138, Miami, Florida, USA. Association for Computatio… [find](https://scholar.google.com/scholar?q=Johnathan+Xie%2C+Annie+S+Chen%2C+Yoonho+Lee%2C+Eric+Mitchell%2C+and+Chelsea+Finn%2E+2024%2E+Calibrating+language+models+with+adaptive+temperature+scaling%2E+In+Proceedings+of+the+2024+Conference+on+Empirical+Method)
184. Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang. 2024a. [Benchmarking retrieval-augmented generation for medicine](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/). ArXiv, abs/2402.13178.
185. Miao Xiong, Zhiyuan Hu, Xinyang Lu, YIFEI LI, Jie Fu, Junxian He, and Bryan Hooi. 2024b. Can LLMs express their uncertainty? an empirical evaluation of confidence elicitation in LLMs. In The Twelfth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Miao+Xiong%2C+Zhiyuan+Hu%2C+Xinyang+Lu%2C+YIFEI+LI%2C+Jie+Fu%2C+Junxian+He%2C+and+Bryan+Hooi%2E+2024b%2E+Can+LLMs+express+their+uncertainty%3F+an+empirical+evaluation+of+confidence+elicitation+in+LLMs%2E+In+The+Twelfth+I)
186. Hongshen Xu, Zichen Zhu, Da Ma, Situo Zhang, Shuai Fan, Lu Chen, and Kai Yu. 2024a. Rejection improves reliability: Training llms to refuse unknown questions using rl from knowledge feedback. ArXiv, abs/2403.18349. — [arXiv:2403.18349](https://arxiv.org/abs/2403.18349) [find](https://scholar.google.com/scholar?q=Hongshen+Xu%2C+Zichen+Zhu%2C+Da+Ma%2C+Situo+Zhang%2C+Shuai+Fan%2C+Lu+Chen%2C+and+Kai+Yu%2E+2024a%2E+Rejection+improves+reliability%3A+Training+llms+to+refuse+unknown+questions+using+rl+from+knowledge+feedback%2E+ArXiv%2C+a)
187. Rongwu Xu, Brian S. Lin, Shujian Yang, Tianqi Zhang, Weiyan Shi, Tianwei Zhang, Zhixuan Fang, Wei Xu, and Han Qiu. 2024b. The earth is flat because...: Investigating llms’ belief towards misinformation via persuasive conversation. In Proceedings of the 62nd Annual Meeting of the Association for Comp… [find](https://scholar.google.com/scholar?q=Rongwu+Xu%2C+Brian+S%2E+Lin%2C+Shujian+Yang%2C+Tianqi+Zhang%2C+Weiyan+Shi%2C+Tianwei+Zhang%2C+Zhixuan+Fang%2C+Wei+Xu%2C+and+Han+Qiu%2E+2024b%2E+The+earth+is+flat+because%2E%2E%2E%3A+Investigating+llms%E2%80%99+belief+towards+misinformatio)
188. Xin Xu, Yue Liu, Panupong Pasupat, Mehran Kazemi, et al. 2024c. [In-context learning with retrieved demonstrations for language models: A survey](https://md.research.elamur.ai/free/en/papers/luo-2024-context-learning-retrieved-demonstrations-language/). arXiv preprint arXiv:2401.11624.
189. Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, and Csaba Szepesvári. 2024. To believe or not to believe your llm. arXiv preprint arXiv:2406.02543. [find](https://scholar.google.com/scholar?q=Yasin+Abbasi+Yadkori%2C+Ilja+Kuzborskij%2C+Andr%C3%A1s+Gy%C3%B6rgy%2C+and+Csaba+Szepesv%C3%A1ri%2E+2024%2E+To+believe+or+not+to+believe+your+llm%2E+arXiv+preprint+arXiv%3A2406%2E02543%2E)
190. Yuzi Yan, Xingzhou Lou, Jialian Li, Yiping Zhang, Jian Xie, Chao Yu, Yu Wang, Dong Yan, and Yuan Shen. 2024. Reward-robust rlhf in llms. arXiv preprint arXiv:2409.15360. — [arXiv:2409.15360](https://arxiv.org/abs/2409.15360) [find](https://scholar.google.com/scholar?q=Yuzi+Yan%2C+Xingzhou+Lou%2C+Jialian+Li%2C+Yiping+Zhang%2C+Jian+Xie%2C+Chao+Yu%2C+Yu+Wang%2C+Dong+Yan%2C+and+Yuan+Shen%2E+2024%2E+Reward%2Drobust+rlhf+in+llms%2E+arXiv+preprint+arXiv%3A2409%2E15360%2E+%E2%80%94+%5BarXiv%3A2409%2E15360%5D%28https%3A%2F%2Fa)
191. Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. 2024a. [Large language models as optimizers](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/). In The Twelfth International Conference on Learning Representations.
192. Dingkang Yang, Dongling Xiao, Jinjie Wei, Mingcheng Li, Zhaoyu Chen, Ke Li, and Lihua Zhang. 2024b. Improving factuality in large language models via decoding-time hallucinatory and truthful comparators. arXiv preprint arXiv:2408.12325. — [arXiv:2408.12325](https://arxiv.org/abs/2408.12325) [find](https://scholar.google.com/scholar?q=Dingkang+Yang%2C+Dongling+Xiao%2C+Jinjie+Wei%2C+Mingcheng+Li%2C+Zhaoyu+Chen%2C+Ke+Li%2C+and+Lihua+Zhang%2E+2024b%2E+Improving+factuality+in+large+language+models+via+decoding%2Dtime+hallucinatory+and+truthful+comparato)
193. Haoyan Yang, Zhitao Li, Yong Zhang, Jianzong Wang, Ning Cheng, Ming Li, and Jing Xiao. 2023a. PRCA: Fitting black-box large language models for retrieval question answering via pluggable reward-driven contextual adapter. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language… [find](https://scholar.google.com/scholar?q=Haoyan+Yang%2C+Zhitao+Li%2C+Yong+Zhang%2C+Jianzong+Wang%2C+Ning+Cheng%2C+Ming+Li%2C+and+Jing+Xiao%2E+2023a%2E+PRCA%3A+Fitting+black%2Dbox+large+language+models+for+retrieval+question+answering+via+pluggable+reward%2Ddriven)
194. Muchen Yang, Moxin Li, Yongle Li, Zijun Chen, Chongming Gao, Junqi Zhang, Yangyang Li, and Fuli Feng. 2024c. Dual-phase accelerated prompt optimization. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 12163–12173, Miami, Florida, USA. Association for Computational Lin… [find](https://scholar.google.com/scholar?q=Muchen+Yang%2C+Moxin+Li%2C+Yongle+Li%2C+Zijun+Chen%2C+Chongming+Gao%2C+Junqi+Zhang%2C+Yangyang+Li%2C+and+Fuli+Feng%2E+2024c%2E+Dual%2Dphase+accelerated+prompt+optimization%2E+In+Findings+of+the+Association+for+Computationa)
195. Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, and Pengfei Liu. 2023b. Alignment for honesty. ArXiv, abs/2312.07000. [find](https://scholar.google.com/scholar?q=Yuqing+Yang%2C+Ethan+Chern%2C+Xipeng+Qiu%2C+Graham+Neubig%2C+and+Pengfei+Liu%2E+2023b%2E+Alignment+for+honesty%2E+ArXiv%2C+abs%2F2312%2E07000%2E)
196. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. [Hotpotqa: A dataset for diverse, explainable multi-hop question answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussel…
197. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023,… [find](https://scholar.google.com/scholar?q=Shunyu+Yao%2C+Dian+Yu%2C+Jeffrey+Zhao%2C+Izhak+Shafran%2C+Tom+Griffiths%2C+Yuan+Cao%2C+and+Karthik+Narasimhan%2E+2023%2E+Tree+of+thoughts%3A+Deliberate+problem+solving+with+large+language+models%2E+In+Advances+in+Neural+)
198. Zijun Yao, Weijian Qi, Liangming Pan, Shulin Cao, Linmei Hu, Weichuan Liu, Lei Hou, and Juanzi Li. 2024. [Seakr: Self-aware knowledge retrieval for adaptive retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/yao-2025-seakr-self-aware-knowledge-retrieval/). arXiv preprint arXiv:2406.19215.
199. Fanghua Ye, Mingming Yang, Jianhui Pang, Longyue Wang, Derek F. Wong, Emine Yilmaz, Shuming Shi, and Zhaopeng Tu. 2024. Benchmarking llms via uncertainty quantification. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS… [find](https://scholar.google.com/scholar?q=Fanghua+Ye%2C+Mingming+Yang%2C+Jianhui+Pang%2C+Longyue+Wang%2C+Derek+F%2E+Wong%2C+Emine+Yilmaz%2C+Shuming+Shi%2C+and+Zhaopeng+Tu%2E+2024%2E+Benchmarking+llms+via+uncertainty+quantification%2E+In+Advances+in+Neural+Informat)
200. Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2023. Compositional exemplars for in-context learning. In International Conference on Machine Learning, pages 39818–39833. PMLR. [find](https://scholar.google.com/scholar?q=Jiacheng+Ye%2C+Zhiyong+Wu%2C+Jiangtao+Feng%2C+Tao+Yu%2C+and+Lingpeng+Kong%2E+2023%2E+Compositional+exemplars+for+in%2Dcontext+learning%2E+In+International+Conference+on+Machine+Learning%2C+pages+39818%E2%80%9339833%2E+PMLR%2E)
201. Xunjian Yin, Xu Zhang, Jie Ruan, and Xiaojun Wan. 2024. Benchmarking knowledge boundary for large language models: A different perspective on model evaluation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thail… [find](https://scholar.google.com/scholar?q=Xunjian+Yin%2C+Xu+Zhang%2C+Jie+Ruan%2C+and+Xiaojun+Wan%2E+2024%2E+Benchmarking+knowledge+boundary+for+large+language+models%3A+A+different+perspective+on+model+evaluation%2E+In+Proceedings+of+the+62nd+Annual+Meetin)
202. Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang. 2023. Do large language models know what they don’t know? In Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023, pages 8653–8665. Association for Computational Linguist… [find](https://scholar.google.com/scholar?q=Zhangyue+Yin%2C+Qiushi+Sun%2C+Qipeng+Guo%2C+Jiawen+Wu%2C+Xipeng+Qiu%2C+and+Xuanjing+Huang%2E+2023%2E+Do+large+language+models+know+what+they+don%E2%80%99t+know%3F+In+Findings+of+the+Association+for+Computational+Linguistics%3A)
203. Lang Yu, Qin Chen, Jie Zhou, and Liang He. 2024. MELO: enhancing model editing with neuron-indexed dynamic lora. In Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, pages 19449–19457. AAAI Press. [find](https://scholar.google.com/scholar?q=Lang+Yu%2C+Qin+Chen%2C+Jie+Zhou%2C+and+Liang+He%2E+2024%2E+MELO%3A+enhancing+model+editing+with+neuron%2Dindexed+dynamic+lora%2E+In+Thirty%2DEighth+AAAI+Conference+on+Artificial+Intelligence%2C+AAAI+2024%2C+pages+19449%E2%80%93194)
204. Hanning Zhang, Shizhe Diao, Yong Lin, Yi R. Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, and Tong Zhang. 2024a. R-tuning: Instructing large language models to say ’i don’t know’. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguist… [find](https://scholar.google.com/scholar?q=Hanning+Zhang%2C+Shizhe+Diao%2C+Yong+Lin%2C+Yi+R%2E+Fung%2C+Qing+Lian%2C+Xingyao+Wang%2C+Yangyi+Chen%2C+Heng+Ji%2C+and+Tong+Zhang%2E+2024a%2E+R%2Dtuning%3A+Instructing+large+language+models+to+say+%E2%80%99i+don%E2%80%99t+know%E2%80%99%2E+In+Proceeding)
205. Jiaxin Zhang, Wendi Cui, Yiran Huang, Kamalika Das, and Kumar Sricharan. 2024b. Synthetic knowledge ingestion: Towards knowledge refinement and injection for enhancing large language models. In Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Jiaxin+Zhang%2C+Wendi+Cui%2C+Yiran+Huang%2C+Kamalika+Das%2C+and+Kumar+Sricharan%2E+2024b%2E+Synthetic+knowledge+ingestion%3A+Towards+knowledge+refinement+and+injection+for+enhancing+large+language+models%2E+In+Confer)
206. Kun Zhang, Jiali Zeng, Fandong Meng, Yuanzhuo Wang, Shiqi Sun, Long Bai, Huawei Shen, and Jie Zhou. 2024c. Tree-of-reasoning question decomposition for complex question answering with large language models. In Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, pages 19560– 19568. A… [find](https://scholar.google.com/scholar?q=Kun+Zhang%2C+Jiali+Zeng%2C+Fandong+Meng%2C+Yuanzhuo+Wang%2C+Shiqi+Sun%2C+Long+Bai%2C+Huawei+Shen%2C+and+Jie+Zhou%2E+2024c%2E+Tree%2Dof%2Dreasoning+question+decomposition+for+complex+question+answering+with+large+language+m)
207. Mozhi Zhang, Mianqiu Huang, Rundong Shi, Linsen Guo, Chong Peng, Peng Yan, Yaqian Zhou, and Xipeng Qiu. 2024d. Calibrating the confidence of large language models by eliciting fidelity. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL,… [find](https://scholar.google.com/scholar?q=Mozhi+Zhang%2C+Mianqiu+Huang%2C+Rundong+Shi%2C+Linsen+Guo%2C+Chong+Peng%2C+Peng+Yan%2C+Yaqian+Zhou%2C+and+Xipeng+Qiu%2E+2024d%2E+Calibrating+the+confidence+of+large+language+models+by+eliciting+fidelity%2E+In+Proceedings)
208. Shaolei Zhang, Tian Yu, and Yang Feng. 2024e. [Truthx: Alleviating hallucinations by editing large language models in truthful space](https://md.research.elamur.ai/free/en/papers/zhang-2024-truthx-alleviating-hallucinations-editing-large/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024, pa…
209. Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, and Joseph E. Gonzalez. 2023a. TEMPERA: Test-time prompt editing via reinforcement learning. In The Eleventh International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Tianjun+Zhang%2C+Xuezhi+Wang%2C+Denny+Zhou%2C+Dale+Schuurmans%2C+and+Joseph+E%2E+Gonzalez%2E+2023a%2E+TEMPERA%3A+Test%2Dtime+prompt+editing+via+reinforcement+learning%2E+In+The+Eleventh+International+Conference+on+Learni)
210. Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, and Tat-Seng Chua. 2024f. [CLAMBER: A benchmark of identifying and clarifying ambiguous information needs in large language models](https://md.research.elamur.ai/free/en/papers/zhang-2024-clamber-benchmark-identifying-clarifying-ambiguous/). In Proceedings of the 62nd Annual Meeting of the Association for Com…
211. Yue Zhang, Leyang Cui, Wei Bi, and Shuming Shi. 2023b. Alleviating hallucinations of large language models through induced hallucinations. CoRR, abs/2312.15710. [find](https://scholar.google.com/scholar?q=Yue+Zhang%2C+Leyang+Cui%2C+Wei+Bi%2C+and+Shuming+Shi%2E+2023b%2E+Alleviating+hallucinations+of+large+language+models+through+induced+hallucinations%2E+CoRR%2C+abs%2F2312%2E15710%2E)
212. Bowen Zhao, Zander Brumbaugh, Yizhong Wang, Hannaneh Hajishirzi, and Noah A. Smith. 2024a. Set the clock: Temporal alignment of pretrained language models. In Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024, pages 15015–… [find](https://scholar.google.com/scholar?q=Bowen+Zhao%2C+Zander+Brumbaugh%2C+Yizhong+Wang%2C+Hannaneh+Hajishirzi%2C+and+Noah+A%2E+Smith%2E+2024a%2E+Set+the+clock%3A+Temporal+alignment+of+pretrained+language+models%2E+In+Findings+of+the+Association+for+Computati)
213. Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, and Lidong Bing. 2023. [Verify-and-edit: A knowledge-enhanced chain-of-thought framework](https://md.research.elamur.ai/free/en/papers/zhao-2023-verify-edit-knowledge-enhanced-chain/). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 202…
214. Xinran Zhao, Hongming Zhang, Xiaoman Pan, Wenlin Yao, Dong Yu, Tongshuang Wu, and Jianshu Chen. 2024b. Fact-and-reflection (FaR) improves confidence calibration of large language models. In Findings of the Association for Computational Linguistics ACL 2024, pages 8702–8718, Bangkok, Thailand and vir… [find](https://scholar.google.com/scholar?q=Xinran+Zhao%2C+Hongming+Zhang%2C+Xiaoman+Pan%2C+Wenlin+Yao%2C+Dong+Yu%2C+Tongshuang+Wu%2C+and+Jianshu+Chen%2E+2024b%2E+Fact%2Dand%2Dreflection+%28FaR%29+improves+confidence+calibration+of+large+language+models%2E+In+Findings+o)
215. Yong Zhao, Yang Deng, See-Kiong Ng, and Tat-Seng Chua. 2025. Aligning large language models for faithful integrity against opposing argument. In AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, pages 27748– 27756. AAAI Press. [find](https://scholar.google.com/scholar?q=Yong+Zhao%2C+Yang+Deng%2C+See%2DKiong+Ng%2C+and+Tat%2DSeng+Chua%2E+2025%2E+Aligning+large+language+models+for+faithful+integrity+against+opposing+argument%2E+In+AAAI%2D25%2C+Sponsored+by+the+Association+for+the+Advanceme)
216. Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, and Dawei Yin. 2024c. Knowing what llms DO NOT know: A simple yet effective self-detection method. In Proceedings of the 2024 Conference of the North American Chapter of the Association for… [find](https://scholar.google.com/scholar?q=Yukun+Zhao%2C+Lingyong+Yan%2C+Weiwei+Sun%2C+Guoliang+Xing%2C+Chong+Meng%2C+Shuaiqiang+Wang%2C+Zhicong+Cheng%2C+Zhaochun+Ren%2C+and+Dawei+Yin%2E+2024c%2E+Knowing+what+llms+DO+NOT+know%3A+A+simple+yet+effective+self%2Ddetectio)
217. Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023a. Can we edit factual knowledge by in-context learning? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023, pages 4862– 4876.… [find](https://scholar.google.com/scholar?q=Ce+Zheng%2C+Lei+Li%2C+Qingxiu+Dong%2C+Yuxuan+Fan%2C+Zhiyong+Wu%2C+Jingjing+Xu%2C+and+Baobao+Chang%2E+2023a%2E+Can+we+edit+factual+knowledge+by+in%2Dcontext+learning%3F+In+Proceedings+of+the+2023+Conference+on+Empirical+M)
218. Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li. 2023b. Progressive-hint prompting improves reasoning in large language models. CoRR, abs/2304.09797. [find](https://scholar.google.com/scholar?q=Chuanyang+Zheng%2C+Zhengying+Liu%2C+Enze+Xie%2C+Zhenguo+Li%2C+and+Yu+Li%2E+2023b%2E+Progressive%2Dhint+prompting+improves+reasoning+in+large+language+models%2E+CoRR%2C+abs%2F2304%2E09797%2E)
219. Haoxiang Zhong, Chaojun Xiao, Cunchao Tu, T. Zhang, Zhiyuan Liu, and Maosong Sun. 2019. Jec-qa: A legal-domain question answering dataset. ArXiv, abs/1911.12011. [find](https://scholar.google.com/scholar?q=Haoxiang+Zhong%2C+Chaojun+Xiao%2C+Cunchao+Tu%2C+T%2E+Zhang%2C+Zhiyuan+Liu%2C+and+Maosong+Sun%2E+2019%2E+Jec%2Dqa%3A+A+legal%2Ddomain+question+answering+dataset%2E+ArXiv%2C+abs%2F1911%2E12011%2E)
220. Zexuan Zhong, Zhengxuan Wu, Christopher D. Manning, Christopher Potts, and Danqi Chen. 2023. Mquake: Assessing knowledge editing in language models via multi-hop questions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10… [find](https://scholar.google.com/scholar?q=Zexuan+Zhong%2C+Zhengxuan+Wu%2C+Christopher+D%2E+Manning%2C+Christopher+Potts%2C+and+Danqi+Chen%2E+2023%2E+Mquake%3A+Assessing+knowledge+editing+in+language+models+via+multi%2Dhop+questions%2E+In+Proceedings+of+the+2023+)
221. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi. 2023a. [Least-to-most prompting enables complex reasoning in large language models](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/). In The Eleventh International Conference on Learning Representati…
222. Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, and Maarten Sap. 2024. Relying on the unreliable: The impact of language models’ reluctance to express uncertainty. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand,… [find](https://scholar.google.com/scholar?q=Kaitlyn+Zhou%2C+Jena+D%2E+Hwang%2C+Xiang+Ren%2C+and+Maarten+Sap%2E+2024%2E+Relying+on+the+unreliable%3A+The+impact+of+language+models%E2%80%99+reluctance+to+express+uncertainty%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of)
223. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2023b. [Large language models are human-level prompt engineers](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/). In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net.
224. Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat seng Chua. 2021. [Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance](https://md.research.elamur.ai/free/en/papers/zhu-2021-tat-qa-question-answering-benchmark/). ArXiv, abs/2105.07624.
225. Runchuan Zhu, Zinco Jiang, Jiang Wu, Zhipeng Ma, Jiahe Song, Fengshuo Bai, Dahua Lin, Lijun Wu, and Conghui He. 2025. Grait: Gradient-driven refusal-aware instruction tuning for effective hallucination mitigation. Preprint, arXiv:2502.05911. [find](https://scholar.google.com/scholar?q=Runchuan+Zhu%2C+Zinco+Jiang%2C+Jiang+Wu%2C+Zhipeng+Ma%2C+Jiahe+Song%2C+Fengshuo+Bai%2C+Dahua+Lin%2C+Lijun+Wu%2C+and+Conghui+He%2E+2025%2E+Grait%3A+Gradient%2Ddriven+refusal%2Daware+instruction+tuning+for+effective+hallucinatio)
226. Runchuan Zhu, Zhipeng Ma, Jiang Wu, Junyuan Gao, Jiaqi Wang, Dahua Lin, and Conghui He. 2024. Utilize the flow before stepping into the same river twice: Certainty represented knowledge flow for refusal-aware instruction tuning. CoRR, abs/2410.06913. — [arXiv:2410.06913](https://arxiv.org/abs/2410.06913) [find](https://scholar.google.com/scholar?q=Runchuan+Zhu%2C+Zhipeng+Ma%2C+Jiang+Wu%2C+Junyuan+Gao%2C+Jiaqi+Wang%2C+Dahua+Lin%2C+and+Conghui+He%2E+2024%2E+Utilize+the+flow+before+stepping+into+the+same+river+twice%3A+Certainty+represented+knowledge+flow+for+refus)
227. Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, and Richard S. Zemel. 2024. Prompt risk control: A rigorous framework for responsible deployment of large language models. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11,… [find](https://scholar.google.com/scholar?q=Thomas+P%2E+Zollo%2C+Todd+Morrill%2C+Zhun+Deng%2C+Jake+Snell%2C+Toniann+Pitassi%2C+and+Richard+S%2E+Zemel%2E+2024%2E+Prompt+risk+control%3A+A+rigorous+framework+for+responsible+deployment+of+large+language+models%2E+In+The)

*Updated 1 November 2026*