---
title: "Logical Consistency as a Bridge — paper by Mi et al. (2026)"
description: "The authors propose LaaB (Logical Consistency-as-a-Bridge), which combines both. 77 references."
authors: ["Hao Mi", "Qiang Sheng", "Shaofei Wang", "Beizhe Hu", "Yifan Sun", "Zhengjia Wang", "Hengqi Zeng", "Yang Li", "Danding Wang", "Juan Cao"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.286/"
doi: "10.18653/v1/2026.acl-long.286"
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/mi-2026-logical-consistency-as-bridge-improving/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mi-2026-logical-consistency-as-bridge-improving/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/35/) / Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments

# Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments

**Authors:** Hao Mi, [Qiang Sheng](https://md.research.elamur.ai/free/en/authors/qiang-sheng/), Shaofei Wang, [Beizhe Hu](https://md.research.elamur.ai/free/en/authors/beizhe-hu/), [Yifan Sun](https://md.research.elamur.ai/free/en/authors/yifan-sun/), Zhengjia Wang, Hengqi Zeng, [Yang Li](https://md.research.elamur.ai/free/en/authors/yang-li/), [Danding Wang](https://md.research.elamur.ai/free/en/authors/danding-wang/) and [Juan Cao](https://md.research.elamur.ai/free/en/authors/juan-cao/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **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/2026.acl-long.286/) · [PDF](https://aclanthology.org/2026.acl-long.286.pdf) · [DOI 10.18653/v1/2026.acl-long.286](https://doi.org/10.18653/v1/2026.acl-long.286) · [OpenAlex](https://openalex.org/W7166867317)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper tackles factual hallucination detection in LLMs, framed as binary classification of whether a response is factual. Existing detectors follow one of two routes. Some read internal signals such as hidden states or logits, which can miss confidently wrong answers. Others prompt the model to judge itself, which inherits self-preference bias. The authors propose LaaB (Logical Consistency-as-a-Bridge), which combines both. The model's own Yes/No verdict on its answer is treated as just another generation that might be hallucinated, so a second detector analyzes the internal features of that verdict (a "meta-judgment"). The two views are linked by a simple rule: a "Yes" verdict means the answer and the verdict share the same factuality label, while a "No" means the labels are opposite. The two detectors are trained jointly through logic-constrained mutual learning. Experiments use TriviaQA, MMLU, NQ_Open and HaluEval with four open-source LLMs and eight baselines. LaaB improves detection for most base models while adding little inference cost. This makes it practical for LLM providers who want to monitor their own services.

## Key points
- **Architecture:** a response detector D_r works on intrinsic features of the answer, and a judgment detector D_j works on intrinsic features of the self-judgment. The judgment detector's prediction is kept for "Yes" and flipped (1 − S_j) for "No". The two predictions are aligned with a Huber-based logic loss alongside cross-entropy. D_j is discarded after training.
- **Evaluation setup:** 4 public datasets (TriviaQA, MMLU, NQ_Open, HaluEval), 4 LLMs and 8 baselines. Code and data are released at github.com/ICTMCG/LaaB.
- **Error analysis:** a Sankey analysis shows that many of D_r's earlier errors become correct while most of its correct predictions are preserved. A few cases that both views originally got wrong are also fixed.
- **Effect of length:** gains are largest for longer responses, peaking on MMLU instances longer than 30 tokens.
- **Limitations:**
  - Forcing a Yes/No answer may add noise.
  - The logic constraint is soft, so consistency is not guaranteed for every sample.
  - The method needs white-box access to the LLM, so it suits providers rather than third parties.

*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

- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 194 works · 23,906 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 147 works · 8,480 citations
- [Institute of Computing Technology](https://md.research.elamur.ai/free/en/labs/institute-of-computing-technology/) — Research institute, Beijing, China · 51 works · 3,411 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,772 works · [🤝 Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · 577 works

## Cite

Cite as: Hao Mi, Qiang Sheng, Shaofei Wang, Beizhe Hu, Yifan Sun, Zhengjia Wang, Hengqi Zeng, Yang Li, Danding Wang and Juan Cao. 2026. Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments. Proceedings of the 64th 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{mi2026logical,
  title = {{Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments}},
  author = {Hao Mi, Qiang Sheng, Shaofei Wang, Beizhe Hu, Yifan Sun, Zhengjia Wang, Hengqi Zeng, Yang Li, Danding Wang and Juan Cao},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.286},
  url = {https://doi.org/10.18653/v1/2026.acl-long.286}
}
```

- [← previous Logic-Level Evaluation of Logical Table-to-Text Generation](https://md.research.elamur.ai/free/en/papers/trigg-2026-logic-level-evaluation-logical-table/)
- [next → LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software](https://md.research.elamur.ai/free/en/papers/rashid-2026-logiceval-systematic-framework-evaluating-automated/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/35/)

## References

77 references, 27 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Amos Azaria and Tom 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, pages 967–976. Association for Computational Linguistics.
2. Yuzhuo Bai, Shuzheng Si, Kangyang Luo, Qingyi Wang, Wenhao Li, Gang Chen, Fanchao Qi, and Maosong Sun. 2026. Infi-check: Interpretable and fine-grained fact-checking of llms. Preprint, arXiv:2601.06666. — [arXiv:2601.06666](https://arxiv.org/abs/2601.06666) [find](https://scholar.google.com/scholar?q=Yuzhuo+Bai%2C+Shuzheng+Si%2C+Kangyang+Luo%2C+Qingyi+Wang%2C+Wenhao+Li%2C+Gang+Chen%2C+Fanchao+Qi%2C+and+Maosong+Sun%2E+2026%2E+Infi%2Dcheck%3A+Interpretable+and+fine%2Dgrained+fact%2Dchecking+of+llms%2E+Preprint%2C+arXiv%3A2601%2E0666)
3. Jakub Binkowski, Denis Janiak, Albert Sawczyn, Bogdan Gabrys, and Tomasz Jan Kajdanowicz. 2025. [Hallucination detection in LLMs using spectral features of attention maps](https://md.research.elamur.ai/free/en/papers/binkowski-2025-hallucination-detection-llms-spectral-features/). In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 24354–24385. Association for Com…
4. Aoife Cahill, Leon Derczynski, and Kokil Jaidka. 2025. ACL Policy on Publication Ethics. Accessed: 2026-01-02. [find](https://scholar.google.com/scholar?q=Aoife+Cahill%2C+Leon+Derczynski%2C+and+Kokil+Jaidka%2E+2025%2E+ACL+Policy+on+Publication+Ethics%2E+Accessed%3A+2026%2D01%2D02%2E)
5. Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, Mingyuan Tao, Zhihang Fu, and Jieping Ye. 2024. INSIDE: LLMs’ internal states retain the power of hallucination detection. In The Twelfth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Chao+Chen%2C+Kai+Liu%2C+Ze+Chen%2C+Yi+Gu%2C+Yue+Wu%2C+Mingyuan+Tao%2C+Zhihang+Fu%2C+and+Jieping+Ye%2E+2024%2E+INSIDE%3A+LLMs%E2%80%99+internal+states+retain+the+power+of+hallucination+detection%2E+In+The+Twelfth+International+Conf)
6. Junjie Chen, Haitao Li, Minghao Qin, Yujia Zhou, Yanxue Ren, Wuyue Wang, Yiqun Liu, Yueyue Wu, and Qingyao Ai. 2026. Simulating dispute mediation with llm-based agents for legal research. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 29368–29375. [find](https://scholar.google.com/scholar?q=Junjie+Chen%2C+Haitao+Li%2C+Minghao+Qin%2C+Yujia+Zhou%2C+Yanxue+Ren%2C+Wuyue+Wang%2C+Yiqun+Liu%2C+Yueyue+Wu%2C+and+Qingyao+Ai%2E+2026%2E+Simulating+dispute+mediation+with+llm%2Dbased+agents+for+legal+research%2E+In+Proceedin)
7. Ethan Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, and Pengfei Liu. 2025. [Factool: Factuality detection in generative ai–a tool augmented framework for multi-task and multi-domain scenarios](https://md.research.elamur.ai/free/en/papers/chern-2023-factool-factuality-detection-generative-ai/). In Second Conference on Language Modeling.
8. Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, Ranjay Krishna, Yoon Kim, and James R. Glass. 2024. [Lookback lens: Detecting and mitigating contextual hallucinations in large language models using only attention maps](https://md.research.elamur.ai/free/en/papers/chuang-2024-lookback-lens-detecting-mitigating-contextual/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Proces…
9. Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2024. [Chain-of-verification reduces hallucination in large language models](https://md.research.elamur.ai/free/en/papers/dhuliawala-2023-chain-verification-reduces-hallucination-large/). In Findings of the Association for Computational Linguistics: ACL 2024, pages 3563–3578. Association for Computati…
10. Xuefeng Du, Chaowei Xiao, and Yixuan Li. 2024. Halo-scope: Harnessing unlabeled llm generations for hallucination detection. Advances in Neural Information Processing Systems, 37:102948–102972. [find](https://scholar.google.com/scholar?q=Xuefeng+Du%2C+Chaowei+Xiao%2C+and+Yixuan+Li%2E+2024%2E+Halo%2Dscope%3A+Harnessing+unlabeled+llm+generations+for+hallucination+detection%2E+Advances+in+Neural+Information+Processing+Systems%2C+37%3A102948%E2%80%93102972%2E)
11. Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, and Yarin Gal. 2024. Detecting hallucinations in large language models using semantic entropy. Nature, 630:625–630. [find](https://scholar.google.com/scholar?q=Sebastian+Farquhar%2C+Jannik+Kossen%2C+Lorenz+Kuhn%2C+and+Yarin+Gal%2E+2024%2E+Detecting+hallucinations+in+large+language+models+using+semantic+entropy%2E+Nature%2C+630%3A625%E2%80%93630%2E)
12. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, and 542 o… — [The Llama 3 Herd of Models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
13. Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017. On calibration of modern neural networks. In International conference on machine learning, pages 1321–1330. PMLR. [find](https://scholar.google.com/scholar?q=Chuan+Guo%2C+Geoff+Pleiss%2C+Yu+Sun%2C+and+Kilian+Q+Weinberger%2E+2017%2E+On+calibration+of+modern+neural+networks%2E+In+International+conference+on+machine+learning%2C+pages+1321%E2%80%931330%2E+PMLR%2E)
14. Qiushan Guo, Xinjiang Wang, Yichao Wu, Zhipeng Yu, Ding Liang, Xiaolin Hu, and Ping Luo. 2020. Online knowledge distillation via collaborative learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11017–11026. [find](https://scholar.google.com/scholar?q=Qiushan+Guo%2C+Xinjiang+Wang%2C+Yichao+Wu%2C+Zhipeng+Yu%2C+Ding+Liang%2C+Xiaolin+Hu%2C+and+Ping+Luo%2E+2020%2E+Online+knowledge+distillation+via+collaborative+learning%2E+In+Proceedings+of+the+IEEE%2FCVF+conference+on+co)
15. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. [Measuring massive multitask language understanding](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/). In International Conference on Learning Representations.
16. Beizhe Hu, Qiang Sheng, Juan Cao, Yang Li, and Danding Wang. 2025. Llm-generated fake news induces truth decay in news ecosystem: A case study on neural news recommendation. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, page 435–4… [find](https://scholar.google.com/scholar?q=Beizhe+Hu%2C+Qiang+Sheng%2C+Juan+Cao%2C+Yang+Li%2C+and+Danding+Wang%2E+2025%2E+Llm%2Dgenerated+fake+news+induces+truth+decay+in+news+ecosystem%3A+A+case+study+on+neural+news+recommendation%2E+In+Proceedings+of+the+48th)
17. Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, and Peng Qi. 2024a. Bad actor, good advisor: Exploring the role of large language models in fake news detection. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pages 22105–22113. [find](https://scholar.google.com/scholar?q=Beizhe+Hu%2C+Qiang+Sheng%2C+Juan+Cao%2C+Yuhui+Shi%2C+Yang+Li%2C+Danding+Wang%2C+and+Peng+Qi%2E+2024a%2E+Bad+actor%2C+good+advisor%3A+Exploring+the+role+of+large+language+models+in+fake+news+detection%2E+In+Proceedings+of+t)
18. Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, and Zheng Zhang. 2024b. Knowledge-centric hallucination detection. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6953–6975. Association for Comput… [find](https://scholar.google.com/scholar?q=Xiangkun+Hu%2C+Dongyu+Ru%2C+Lin+Qiu%2C+Qipeng+Guo%2C+Tianhang+Zhang%2C+Yang+Xu%2C+Yun+Luo%2C+Pengfei+Liu%2C+Yue+Zhang%2C+and+Zheng+Zhang%2E+2024b%2E+Knowledge%2Dcentric+hallucination+detection%2E+In+Proceedings+of+the+2024+Con)
19. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025. [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/). ACM Trans. Inf. Syst., 43(2).
20. Peter J Huber. 1964. Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1):73–101. [find](https://scholar.google.com/scholar?q=Peter+J+Huber%2E+1964%2E+Robust+estimation+of+a+location+parameter%2E+The+Annals+of+Mathematical+Statistics%2C+35%281%29%3A73%E2%80%93101%2E)
21. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023a. [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).
22. Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung. 2023b. [Towards mitigating LLM hallucination via self reflection](https://md.research.elamur.ai/free/en/papers/ji-2023-mitigating-llm-hallucination-self-reflection/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1827–1843. Association for Computational Linguistics.
23. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix,… [find](https://scholar.google.com/scholar?q=Albert+Q%2E+Jiang%2C+Alexandre+Sablayrolles%2C+Arthur+Mensch%2C+Chris+Bamford%2C+Devendra+Singh+Chaplot%2C+Diego+de+las+Casas%2C+Florian+Bressand%2C+Gianna+Lengyel%2C+Guillaume+Lample%2C+Lucile+Saulnier%2C+L%C3%A9lio+Renard+Lav)
24. Che Jiang, Biqing Qi, Xiangyu Hong, Dayuan Fu, Yang Cheng, Fandong Meng, Mo Yu, Bowen Zhou, and Jie Zhou. 2024. On large language models’ hallucination with regard to known facts. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Hu… [find](https://scholar.google.com/scholar?q=Che+Jiang%2C+Biqing+Qi%2C+Xiangyu+Hong%2C+Dayuan+Fu%2C+Yang+Cheng%2C+Fandong+Meng%2C+Mo+Yu%2C+Bowen+Zhou%2C+and+Jie+Zhou%2E+2024%2E+On+large+language+models%E2%80%99+hallucination+with+regard+to+known+facts%2E+In+Proceedings+of+th)
25. Mandar Joshi, Eunsol Choi, Daniel 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/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1601–1611. Associati…
26. Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, and 17 others. 2022.… [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+Scott+Johnston%2C+Sheer+El%2DShowk%2C+Andy+Jones%2C+)
27. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. [Natural…](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
28. Junyi Li, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023a. [HaluEval: A large-scale hallucination evaluation benchmark for large language models](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6449–6464. Association for Computational Lin…
29. Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. 2023b. [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.
30. Miaoran Li, Jiangning Chen, Minghua Xu, and Xiaolong Wang. 2025a. Hallucination detection in structured query generation via LLM self-debating. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 16102–16113. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Miaoran+Li%2C+Jiangning+Chen%2C+Minghua+Xu%2C+and+Xiaolong+Wang%2E+2025a%2E+Hallucination+detection+in+structured+query+generation+via+LLM+self%2Ddebating%2E+In+Findings+of+the+Association+for+Computational+Linguis)
31. Rui Li, Jing Long, Muge Qi, Heming Xia, Lei Sha, Peiyi Wang, and Zhifang Sui. 2025b. Towards harmonized uncertainty estimation for large language models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22938–22953. Association… [find](https://scholar.google.com/scholar?q=Rui+Li%2C+Jing+Long%2C+Muge+Qi%2C+Heming+Xia%2C+Lei+Sha%2C+Peiyi+Wang%2C+and+Zhifang+Sui%2E+2025b%2E+Towards+harmonized+uncertainty+estimation+for+large+language+models%2E+In+Proceedings+of+the+63rd+Annual+Meeting+of+t)
32. Yibo Li, Miao Xiong, Jiaying Wu, and Bryan Hooi. 2025c. Conftuner: Training large language models to express their confidence verbally. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Yibo+Li%2C+Miao+Xiong%2C+Jiaying+Wu%2C+and+Bryan+Hooi%2E+2025c%2E+Conftuner%3A+Training+large+language+models+to+express+their+confidence+verbally%2E+In+The+Thirty%2Dninth+Annual+Conference+on+Neural+Information+Proc)
33. Aiwei Liu, Qiang Sheng, and Xuming Hu. 2024a. Preventing and detecting misinformation generated by large language models. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, page 3001–3004. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Aiwei+Liu%2C+Qiang+Sheng%2C+and+Xuming+Hu%2E+2024a%2E+Preventing+and+detecting+misinformation+generated+by+large+language+models%2E+In+Proceedings+of+the+47th+International+ACM+SIGIR+Conference+on+Research+and+)
34. 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, pages 18199–18224. Association for Computational Linguistics. [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)
35. Qiang Liu, Xinlong Chen, Yue Ding, Bowen Song, Weiqiang Wang, Shu Wu, and Liang Wang. 2025a. Attention-guided self-reflection for zero-shot hallucination detection in large language models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21005– 21021.… [find](https://scholar.google.com/scholar?q=Qiang+Liu%2C+Xinlong+Chen%2C+Yue+Ding%2C+Bowen+Song%2C+Weiqiang+Wang%2C+Shu+Wu%2C+and+Liang+Wang%2E+2025a%2E+Attention%2Dguided+self%2Dreflection+for+zero%2Dshot+hallucination+detection+in+large+language+models%2E+In+Proceed)
36. Zihang Liu, Jiawei Guo, Hao Zhang, Hongyang Chen, Jiajun Bu, and Haishuai Wang. 2025b. Long-form hallucination detection with self-elicitation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 4082–4100. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Zihang+Liu%2C+Jiawei+Guo%2C+Hao+Zhang%2C+Hongyang+Chen%2C+Jiajun+Bu%2C+and+Haishuai+Wang%2E+2025b%2E+Long%2Dform+hallucination+detection+with+self%2Delicitation%2E+In+Findings+of+the+Association+for+Computational+Linguis)
37. Huan Ma, Jingdong Chen, Joey Tianyi Zhou, Guangyu Wang, and Changqing Zhang. 2025. Estimating llm uncertainty with evidence. Preprint, arXiv:2502.00290. — [arXiv:2502.00290](https://arxiv.org/abs/2502.00290) [find](https://scholar.google.com/scholar?q=Huan+Ma%2C+Jingdong+Chen%2C+Joey+Tianyi+Zhou%2C+Guangyu+Wang%2C+and+Changqing+Zhang%2E+2025%2E+Estimating+llm+uncertainty+with+evidence%2E+Preprint%2C+arXiv%3A2502%2E00290%2E+%E2%80%94+%5BarXiv%3A2502%2E00290%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2502)
38. 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. Association for Computational Linguist… [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)
39. Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang 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/). In Proceedings of the 2023 Conference on Empirical Methods in Natural L…
40. Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal, Zeeshan Memon, Muhammad Ibtsaam Qadir, Sagnik Bhattacharya, Hassan Rizwan, Abhiram R. [find](https://scholar.google.com/scholar?q=Muhammad+Ahmed+Mohsin%2C+Muhammad+Umer%2C+Ahsan+Bilal%2C+Zeeshan+Memon%2C+Muhammad+Ibtsaam+Qadir%2C+Sagnik+Bhattacharya%2C+Hassan+Rizwan%2C+Abhiram+R%2E)
41. Gorle, Maahe Zehra Kazmi, Nukhba Amir, Ali Subhan, Muhammad Usman Rafique, Zihao He, Pulkit Mehta, Muhammad Ali Jamshed, and John M. Cioffi. 2026. On the fundamental limits of llms at scale. Preprint, arXiv:2511.12869. — [arXiv:2511.12869](https://arxiv.org/abs/2511.12869) [find](https://scholar.google.com/scholar?q=Gorle%2C+Maahe+Zehra+Kazmi%2C+Nukhba+Amir%2C+Ali+Subhan%2C+Muhammad+Usman+Rafique%2C+Zihao+He%2C+Pulkit+Mehta%2C+Muhammad+Ali+Jamshed%2C+and+John+M%2E+Cioffi%2E+2026%2E+On+the+fundamental+limits+of+llms+at+scale%2E+Preprint%2C)
42. Shiyu Ni, Keping Bi, Jiafeng Guo, Lulu Yu, Baolong Bi, and Xueqi Cheng. 2025. [Towards fully exploiting LLM internal states to enhance knowledge boundary perception](https://md.research.elamur.ai/free/en/papers/ni-2025-fully-exploiting-llm-internal-states/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24315–24329.…
43. Arjun Panickssery, Samuel Bowman, and Shi Feng. 2024. Llm evaluators recognize and favor their own generations. Advances in Neural Information Processing Systems, 37:68772–68802. [find](https://scholar.google.com/scholar?q=Arjun+Panickssery%2C+Samuel+Bowman%2C+and+Shi+Feng%2E+2024%2E+Llm+evaluators+recognize+and+favor+their+own+generations%2E+Advances+in+Neural+Information+Processing+Systems%2C+37%3A68772%E2%80%9368802%2E)
44. Seongheon Park, Xuefeng Du, Min-Hsuan Yeh, Haobo Wang, and Yixuan Li. 2025. Steer LLM latents for hallucination detection. In Forty-second International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Seongheon+Park%2C+Xuefeng+Du%2C+Min%2DHsuan+Yeh%2C+Haobo+Wang%2C+and+Yixuan+Li%2E+2025%2E+Steer+LLM+latents+for+hallucination+detection%2E+In+Forty%2Dsecond+International+Conference+on+Machine+Learning%2E)
45. Siya Qi, Yudong Chen, Runcong Zhao, Qinglin Zhu, Zhanghao Hu, Wei Liu, Yulan He, Zheng Yuan, and Lin Gui. 2026. Detecting contextual hallucinations in llms with frequency-aware attention. Preprint, arXiv:2602.18145. — [arXiv:2602.18145](https://arxiv.org/abs/2602.18145) [find](https://scholar.google.com/scholar?q=Siya+Qi%2C+Yudong+Chen%2C+Runcong+Zhao%2C+Qinglin+Zhu%2C+Zhanghao+Hu%2C+Wei+Liu%2C+Yulan+He%2C+Zheng+Yuan%2C+and+Lin+Gui%2E+2026%2E+Detecting+contextual+hallucinations+in+llms+with+frequency%2Daware+attention%2E+Preprint%2C+ar)
46. Qiang Sheng, Xueyao Zhang, Juan Cao, and Lei Zhong. 2021. Integrating pattern-and fact-based fake news detection via model preference learning. In Proceedings of the 30th ACM international conference on information & knowledge management, pages 1640– 1650. [find](https://scholar.google.com/scholar?q=Qiang+Sheng%2C+Xueyao+Zhang%2C+Juan+Cao%2C+and+Lei+Zhong%2E+2021%2E+Integrating+pattern%2Dand+fact%2Dbased+fake+news+detection+via+model+preference+learning%2E+In+Proceedings+of+the+30th+ACM+international+conference+)
47. Yuhui Shi, Qiang Sheng, Juan Cao, Hao Mi, Beizhe Hu, and Danding Wang. 2024. Ten words only still help: improving black-box ai-generated text detection via proxy-guided efficient re-sampling. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, pages 494–502. [find](https://scholar.google.com/scholar?q=Yuhui+Shi%2C+Qiang+Sheng%2C+Juan+Cao%2C+Hao+Mi%2C+Beizhe+Hu%2C+and+Danding+Wang%2E+2024%2E+Ten+words+only+still+help%3A+improving+black%2Dbox+ai%2Dgenerated+text+detection+via+proxy%2Dguided+efficient+re%2Dsampling%2E+In+Proce)
48. Adi Simhi, Itay Itzhak, Fazl Barez, Gabriel Stanovsky, and Yonatan Belinkov. 2025. Trust me, I’m wrong: LLMs hallucinate with certainty despite knowing the answer. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 14665– 14688. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Adi+Simhi%2C+Itay+Itzhak%2C+Fazl+Barez%2C+Gabriel+Stanovsky%2C+and+Yonatan+Belinkov%2E+2025%2E+Trust+me%2C+I%E2%80%99m+wrong%3A+LLMs+hallucinate+with+certainty+despite+knowing+the+answer%2E+In+Findings+of+the+Association+for+C)
49. Jinyan Su, Jennifer Healey, Preslav Nakov, and Claire Cardie. 2025. Between underthinking and overthinking: An empirical study of reasoning length and correctness in llms. Preprint, arXiv:2505.00127. — [arXiv:2505.00127](https://arxiv.org/abs/2505.00127) [find](https://scholar.google.com/scholar?q=Jinyan+Su%2C+Jennifer+Healey%2C+Preslav+Nakov%2C+and+Claire+Cardie%2E+2025%2E+Between+underthinking+and+overthinking%3A+An+empirical+study+of+reasoning+length+and+correctness+in+llms%2E+Preprint%2C+arXiv%3A2505%2E00127%2E+)
50. Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, Yujia Zhou, and Yiqun Liu. 2024. [Unsupervised real-time hallucination detection based on the internal states of large language models](https://md.research.elamur.ai/free/en/papers/su-2024-unsupervised-real-time-hallucination-detection/). In Findings of the Association for Computational Linguistics: ACL 2024, pages 14379–14391. Association f…
51. Xiaoxi Sun, Jinpeng Li, Yan Zhong, Dongyan Zhao, and Rui Yan. 2025. Towards detecting llms hallucination via markov chain-based multi-agent debate framework. In 2025 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 1–5. IEEE. [find](https://scholar.google.com/scholar?q=Xiaoxi+Sun%2C+Jinpeng+Li%2C+Yan+Zhong%2C+Dongyan+Zhao%2C+and+Rui+Yan%2E+2025%2E+Towards+detecting+llms+hallucination+via+markov+chain%2Dbased+multi%2Dagent+debate+framework%2E+In+2025+IEEE+International+Conference+on+A)
52. Chao Tan and Jie Liu. 2022. Online knowledge distillation with elastic peer. Information Sciences, 583:1– 13. [find](https://scholar.google.com/scholar?q=Chao+Tan+and+Jie+Liu%2E+2022%2E+Online+knowledge+distillation+with+elastic+peer%2E+Information+Sciences%2C+583%3A1%E2%80%93+13%2E)
53. Hexiang Tan, Fei Sun, Sha Liu, Du Su, Qi Cao, Xin Chen, Jingang Wang, Xunliang Cai, Yuanzhuo Wang, Huawei Shen, and Xueqi Cheng. 2025. [Too consistent to detect: A study of self-consistent errors in LLMs](https://md.research.elamur.ai/free/en/papers/tan-2025-too-consistent-detect-study-self/). In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages…
54. Hexiang Tan, Wanli Yang, Junwei Zhang, Xin Chen, Rui Tang, Du Su, Jingang Wang, Yuanzhuo Wang, Fei Sun, and Xueqi Cheng. 2026. [Basecal: Unsupervised confidence calibration via base model signals](https://md.research.elamur.ai/free/en/papers/tan-2026-basecal-unsupervised-confidence-calibration-base/). Preprint, arXiv:2601.03042.
55. Roman Vashurin, Maiya Goloburda, Preslav Nakov, and Maxim Panov. 2025. UNCERTAINTY-LINE: Length-invariant estimation of uncertainty for large language models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 7881–7908. Association for Computational Lin… [find](https://scholar.google.com/scholar?q=Roman+Vashurin%2C+Maiya+Goloburda%2C+Preslav+Nakov%2C+and+Maxim+Panov%2E+2025%2E+UNCERTAINTY%2DLINE%3A+Length%2Dinvariant+estimation+of+uncertainty+for+large+language+models%2E+In+Proceedings+of+the+2025+Conference+on+)
56. Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, Gleb Kuzmin, Ivan Lazichny, Alexander Panchenko, Preslav Nakov, Timothy Baldwin, Maxim Panov, and Artem Shelmanov. 2025. [Unconditional truthfulness: Learning unconditional uncertainty of large language models](https://md.research.elamur.ai/free/en/papers/vazhentsev-2025-unconditional-truthfulness-learning-unconditional-uncertainty/). In Proceedings of the 2025 Conference on Em…
57. Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, and Zhijiang Guo. 2025. FaStFact: Faster, stronger long-form factuality evaluations in LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2025, p… [find](https://scholar.google.com/scholar?q=Yingjia+Wan%2C+Haochen+Tan%2C+Xiao+Zhu%2C+Xinyu+Zhou%2C+Zhiwei+Li%2C+Qingsong+Lv%2C+Changxuan+Sun%2C+Jiaqi+Zeng%2C+Yi+Xu%2C+Jianqiao+Lu%2C+Yinhong+Liu%2C+and+Zhijiang+Guo%2E+2025%2E+FaStFact%3A+Faster%2C+stronger+long%2Dform+factual)
58. Pengyu Wang, Linyang Li, Ke Ren, Botian Jiang, Dong Zhang, and Xipeng Qiu. 2023. SeqXGPT: Sentence-level AI-generated text detection. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1144–1156. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Pengyu+Wang%2C+Linyang+Li%2C+Ke+Ren%2C+Botian+Jiang%2C+Dong+Zhang%2C+and+Xipeng+Qiu%2E+2023%2E+SeqXGPT%3A+Sentence%2Dlevel+AI%2Dgenerated+text+detection%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natu)
59. Yiming Wang, Pei Zhang, Baosong Yang, Derek F. Wong, and Rui Wang. 2025. Latent space chain-of-embedding enables output-free LLM self-evaluation. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Yiming+Wang%2C+Pei+Zhang%2C+Baosong+Yang%2C+Derek+F%2E+Wong%2C+and+Rui+Wang%2E+2025%2E+Latent+space+chain%2Dof%2Dembedding+enables+output%2Dfree+LLM+self%2Devaluation%2E+In+The+Thirteenth+International+Conference+on+Learning)
60. Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, and Preslav Nakov. 2024. Factcheck-bench: Fine-grained evaluation benchmark for automat… [find](https://scholar.google.com/scholar?q=Yuxia+Wang%2C+Revanth+Gangi+Reddy%2C+Zain+Muhammad+Mujahid%2C+Arnav+Arora%2C+Aleksandr+Rubashevskii%2C+Jiahui+Geng%2C+Osama+Mohammed+Afzal%2C+Liangming+Pan%2C+Nadav+Borenstein%2C+Aditya+Pillai%2C+Isabelle+Augenstein%2C+Iry)
61. Koki Wataoka, Tsubasa Takahashi, and Ryokan Ri. 2024. [Self-preference bias in LLM-as-a-judge](https://md.research.elamur.ai/free/en/papers/wataoka-2024-self-preference-bias-llm-judge/). In Neurips Safe Generative AI Workshop 2024.
62. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022. [Emergent abilities of large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/). Transactions on Ma…
63. Bingbing Wen, Chenjun Xu, Bin HAN, Robert Wolfe, Lucy Lu Wang, and Bill Howe. 2024. Mitigating overconfidence in large language models: A behavioral lens on confidence estimation and calibration. In NeurIPS 2024 Workshop on Behavioral Machine Learning. [find](https://scholar.google.com/scholar?q=Bingbing+Wen%2C+Chenjun+Xu%2C+Bin+HAN%2C+Robert+Wolfe%2C+Lucy+Lu+Wang%2C+and+Bill+Howe%2E+2024%2E+Mitigating+overconfidence+in+large+language+models%3A+A+behavioral+lens+on+confidence+estimation+and+calibration%2E+In+N)
64. Miao Xiong, Zhiyuan Hu, Xinyang Lu, YIFEI LI, Jie Fu, Junxian He, and Bryan Hooi. 2024. 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+2024%2E+Can+LLMs+express+their+uncertainty%3F+an+empirical+evaluation+of+confidence+elicitation+in+LLMs%2E+In+The+Twelfth+In)
65. Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli. 2025. [Hallucination is inevitable: An innate limitation of large language models](https://md.research.elamur.ai/free/en/papers/xu-2024-hallucination-inevitable-innate-limitation-large/). Preprint, arXiv:2401.11817.
66. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, and 23 others. 2025. Qwen2.5 technical report. Preprint, arXiv:241… [find](https://scholar.google.com/scholar?q=An+Yang%2C+Baosong+Yang%2C+Beichen+Zhang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Haoran+Wei%2C+Huan+Lin%2C+Jian+Yang%2C+Jianhong+Tu%2C+Jianwei+Zhang%2C+Jianxin+Yang%2C+Jiaxi+Yang%2C+Jin)
67. Chuanguang Yang, Zhulin An, Helong Zhou, Fuzhen Zhuang, Yongjun Xu, and Qian Zhang. 2023. Online knowledge distillation via mutual contrastive learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):10212– 10227. [find](https://scholar.google.com/scholar?q=Chuanguang+Yang%2C+Zhulin+An%2C+Helong+Zhou%2C+Fuzhen+Zhuang%2C+Yongjun+Xu%2C+and+Qian+Zhang%2E+2023%2E+Online+knowledge+distillation+via+mutual+contrastive+learning+for+visual+recognition%2E+IEEE+Transactions+on+Pat)
68. 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, pages 8653–8665. Association for Computational Linguistics. [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)
69. Yige Yuan, Bingbing Xu, Hexiang Tan, Fei Sun, Teng Xiao, Wei Li, Huawei Shen, and Xueqi Cheng. 2025. Fact-level calibration and correction for long-form generations. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’25, page 28… [find](https://scholar.google.com/scholar?q=Yige+Yuan%2C+Bingbing+Xu%2C+Hexiang+Tan%2C+Fei+Sun%2C+Teng+Xiao%2C+Wei+Li%2C+Huawei+Shen%2C+and+Xueqi+Cheng%2E+2025%2E+Fact%2Dlevel+calibration+and+correction+for+long%2Dform+generations%2E+In+Proceedings+of+the+48th+Interna)
70. Fujie Zhang, Peiqi Yu, Biao Yi, Baolei Zhang, Tong Li, and Zheli Liu. 2025a. [Prompt-guided internal states for hallucination detection of large language models](https://md.research.elamur.ai/free/en/papers/zhang-2025-prompt-guided-internal-states-hallucination/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21806–21818. Asso…
71. Qingjie Zhang, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, Minlie Huang, Ke Xu, Hewu Li, Liu Yan, and Han Qiu. 2025b. [Understanding the dark side of LLMs’ intrinsic self-correction](https://md.research.elamur.ai/free/en/papers/zhang-2025-understanding-dark-side-llms-intrinsic/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers…
72. Wei Zhang, Guojun Dai, Ding Luo, Yan Wang, and Chen Ye. 2026. From hallucination to certainty: Meta-knowledge guided self-correcting large language models. ACM Transactions on Intelligent Systems and Technology. [find](https://scholar.google.com/scholar?q=Wei+Zhang%2C+Guojun+Dai%2C+Ding+Luo%2C+Yan+Wang%2C+and+Chen+Ye%2E+2026%2E+From+hallucination+to+certainty%3A+Meta%2Dknowledge+guided+self%2Dcorrecting+large+language+models%2E+ACM+Transactions+on+Intelligent+Systems+and+)
73. Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu. 2018. Deep mutual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4320–4328. [find](https://scholar.google.com/scholar?q=Ying+Zhang%2C+Tao+Xiang%2C+Timothy+M+Hospedales%2C+and+Huchuan+Lu%2E+2018%2E+Deep+mutual+learning%2E+In+Proceedings+of+the+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition%2C+pages+4320%E2%80%934328%2E)
74. Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. 2025c. [Siren’s song in the ai ocean: A survey on hallucination in large language models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/). Computational Linguistics, 51(4)…
75. Zhenliang Zhang, Xinyu Hu, Huixuan Zhang, Junzhe Zhang, and Xiaojun Wan. 2025d. [ICR probe: Tracking hidden state dynamics for reliable hallucination detection in LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2025-icr-probe-tracking-hidden-state/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 17986– 180…
76. Weike Zhao, Chaoyi Wu, Yanjie Fan, Pengcheng Qiu, Xiaoman Zhang, Yuze Sun, Xiao Zhou, Shuju Zhang, Yu Peng, Yanfeng Wang, Xin Sun, Ya Zhang, Yongguo Yu, Kun Sun, and Weidi Xie. 2026. An agentic system for rare disease diagnosis with traceable reasoning. Nature, 651:775–784. [find](https://scholar.google.com/scholar?q=Weike+Zhao%2C+Chaoyi+Wu%2C+Yanjie+Fan%2C+Pengcheng+Qiu%2C+Xiaoman+Zhang%2C+Yuze+Sun%2C+Xiao+Zhou%2C+Shuju+Zhang%2C+Yu+Peng%2C+Yanfeng+Wang%2C+Xin+Sun%2C+Ya+Zhang%2C+Yongguo+Yu%2C+Kun+Sun%2C+and+Weidi+Xie%2E+2026%2E+An+agentic+system)
77. 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), pages 3623–3643. Association… [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)

*Updated 1 November 2026*