---
title: "Factuality of Large Language Models: A Survey (2024)"
description: "The work matters because it offers a unified map of the field and concrete directions for future research. 71 references, cited by 40 papers here."
authors: ["Yuxia Wang", "Minghan Wang", "Muhammad Arslan Manzoor", "Fei Liu", "Georgi Nenkov Georgiev", "Rocktim Jyoti Das", "Preslav Nakov"]
year: 2024
venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2024.emnlp-main.1088/"
doi: "10.18653/v1/2024.emnlp-main.1088"
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/wang-2024-factuality-large-language-models-survey/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2024-factuality-large-language-models-survey/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/6/) / Factuality of Large Language Models: A Survey

# Factuality of Large Language Models: A Survey

**Authors:** [Yuxia Wang](https://md.research.elamur.ai/free/en/authors/yuxia-wang/), [Minghan Wang](https://md.research.elamur.ai/free/en/authors/minghan-wang/), [Muhammad Arslan Manzoor](https://md.research.elamur.ai/free/en/authors/muhammad-arslan-manzoor/), [Fei Liu](https://md.research.elamur.ai/free/en/authors/fei-liu/), [Georgi Nenkov Georgiev](https://md.research.elamur.ai/free/en/authors/georgi-nenkov-georgiev/), [Rocktim Jyoti Das](https://md.research.elamur.ai/free/en/authors/rocktim-jyoti-das/) and [Preslav Nakov](https://md.research.elamur.ai/free/en/authors/preslav-nakov/) · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/)) · **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/2024.emnlp-main.1088/) · [PDF](https://aclanthology.org/2024.emnlp-main.1088.pdf) · [DOI 10.18653/v1/2024.emnlp-main.1088](https://doi.org/10.18653/v1/2024.emnlp-main.1088) · [OpenAlex](https://openalex.org/W4404782463)

**Citations:** 37 citations (OpenAlex, 2026-10-14) · [40](https://md.research.elamur.ai/free/en/papers/wang-2024-factuality-large-language-models-survey/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Chat-oriented LLMs often give confident answers that contradict real-world facts, which limits their use in practice. This EMNLP 2024 survey organizes recent research on measuring and improving LLM factuality. It first separates factuality, meaning correct use of world knowledge, from hallucination, meaning output not grounded in the input or context. It also contrasts factuality with relevance and with broader trustworthiness. The authors group evaluation datasets by answer format and explain why scoring long free-form text is hard. They review enhancement techniques applied during pre-training, SFT, inference and post-processing, with a section on vision-language models. They also describe a newer line of work on calibration, where models learn to recognize what they do not know. The survey identifies three core obstacles:
- the language-modeling objective rewards fluent text rather than true text;
- automatic fact-checking of open-ended output is not yet reliable;
- RAG is limited by retrieval latency and by facts that require multi-hop reasoning.

The work matters because it offers a unified map of the field and concrete directions for future research.

## Key points
- Table 1 compares this survey with seven earlier ones, including Tonmoy et al., Huang et al. and Zhang et al. It is the only one covering evaluation, improvement and multimodality together.
- Benchmarks fall into four types:
  - open-ended generation, such as FactScore-Bio, Factcheck-GPT, FacTool-QA, FELM-WK, HaluEval, FreshQA and SelfAware;
  - Yes/No questions, such as Snowball;
  - short-answer or entity-list questions, such as Wiki-category List and Multispan QA;
  - multiple-choice or knowledge benchmarks, such as TruthfulQA, HotpotQA, StrategyQA and MMLU.
- Human-annotated error rates in open-ended datasets vary widely. They range from 12.3% on HaluEval (5,000 items) to 68.0% on FreshQA. HotpotQA was the most frequently used evaluation set among the first 50 references.
- Automatic checkers such as FactScore, FacTool and Factcheck-GPT break a response into atomic claims. Each claim is then verified against evidence from Wikipedia or the web. The authors suggest that a small fine-tuned NLI model could be a cheaper and more robust verifier than GPT-4.
- For multimodal LLMs, the survey covers several groups of methods:
  - evaluation: POPE and GAVIE;
  - fine-tuning: LRV-Instruction and LLaVA-RLHF;
  - inference-time correction: Woodpecker and LURE;
  - representation-level methods: HallE-Switch, VCD and HACL.

*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

- [Monash University](https://md.research.elamur.ai/free/en/universities/monash-university/) — University, Melbourne, Australia · 64 works · 54,972 citations
- [Sofia University "St. Kliment Ohridski"](https://md.research.elamur.ai/free/en/universities/sofia-university-st-kliment-ohridski/) — University, Sofia, Bulgaria · 23 works · 2,694 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works

## Cite

Cite as: Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Nenkov Georgiev, Rocktim Jyoti Das and Preslav Nakov. 2024. Factuality of Large Language Models: A Survey. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{wang2024factuality,
  title = {{Factuality of Large Language Models: A Survey}},
  author = {Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Nenkov Georgiev, Rocktim Jyoti Das and Preslav Nakov},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.1088},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.1088}
}
```

- [← previous BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval](https://md.research.elamur.ai/free/en/papers/su-2024-bright-realistic-challenging-benchmark-reasoning/)
- [next → FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://md.research.elamur.ai/free/en/papers/jin-2024-flashrag-modular-toolkit-efficient-retrieval/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/6/)

## References

71 references, 57 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Sebastian Borgeaud, Arthur Mensch, and Jordan Hoffmann et al. 2021. Improving language models by retrieving from trillions of tokens. In ICML. [find](https://scholar.google.com/scholar?q=Sebastian+Borgeaud%2C+Arthur+Mensch%2C+and+Jordan+Hoffmann+et+al%2E+2021%2E+Improving+language+models+by+retrieving+from+trillions+of+tokens%2E+In+ICML%2E)
2. Tom B. Brown, Benjamin Mann, Nick Ryder, and Melanie Subbiah et al. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). In NeurIPS 2020.
3. Shiqi Chen, Yiran Zhao, Jinghan Zhang, and et al. 2023. [Felm: Benchmarking factuality evaluation of large language models](https://md.research.elamur.ai/free/en/papers/chen-2023-felm-benchmarking-factuality-evaluation-large/). arXiv preprint arXiv:2310.00741.
4. I-Chun Chern, Steffi Chern, and Shiqi Chen et al. 2023. [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/). CoRR, abs/2307.13528.
5. Yung-Sung Chuang, Yujia Xie, and Hongyin Luo et al. 2023. [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/). CoRR, abs/2309.03883.
6. Shehzaad Dhuliawala, Mojtaba Komeili, and et al. 2023. [Chain-of-verification reduces hallucination in large language models](https://md.research.elamur.ai/free/en/papers/dhuliawala-2023-chain-verification-reduces-hallucination-large/). arXiv preprint arXiv:2309.11495.
7. Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. 2023. [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/). CoRR, abs/2305.14325.
8. Mohamed Elaraby, Mengyin Lu, and Jacob Dunn et al. 2023. [Halo: Estimation and reduction of hallucinations in open-source weak large language models](https://md.research.elamur.ai/free/en/papers/elaraby-2023-halo-estimation-reduction-hallucinations-open/). CoRR, abs/2308.11764.
9. Luyu Gao, Zhuyun Dai, and Panupong et al. Pasupat. 2022. [Attributed text generation via post-hoc research and revision](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/). arXiv preprint arXiv:2210.08726.
10. Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. 2023a. [Enabling large language models to generate text with citations](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/). In EMNLP, pages 6465–6488.
11. Yunfan Gao, Yun Xiong, and et al. 2023b. [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). CoRR, abs/2312.10997.
12. Mor Geva, Daniel Khashabi, and et al. 2021. Did aristotle use a laptop? A question answering benchmark with implicit reasoning strategies. TACL, 9:346–361. [find](https://scholar.google.com/scholar?q=Mor+Geva%2C+Daniel+Khashabi%2C+and+et+al%2E+2021%2E+Did+aristotle+use+a+laptop%3F+A+question+answering+benchmark+with+implicit+reasoning+strategies%2E+TACL%2C+9%3A346%E2%80%93361%2E)
13. Anish Gunjal, Jihan Yin, and Erhan Bas. 2023. [Detecting and preventing hallucinations in large vision language models](https://md.research.elamur.ai/free/en/papers/gunjal-2023-detecting-preventing-hallucinations-large-vision/). In AAAI Conference on Artificial Intelligence.
14. Zhijiang Guo, Michael Schlichtkrull, and Andreas Vlachos. 2022. [A survey on automated fact-checking](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/). TACL, 10:178–206.
15. Dan Hendrycks, Collin Burns, and et al. 2021. [Measuring massive multitask language understanding](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/). In ICLR 2021.
16. Ari Holtzman, Jan Buys, and Li et al. 2020. [The curious case of neural text degeneration](https://md.research.elamur.ai/free/en/papers/holtzman-2019-curious-case-neural-text-degeneration/). In ICLR.
17. Lei Huang, Weijiang Yu, Weitao Ma, and Weihong Zhong et al. 2023a. [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/). CoRR, abs/2311.05232.
18. Lei Huang, Weijiang Yu, Weitao Ma, and Weihong Zhong et al. 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/). CoRR, abs/2311.05232.
19. Gautier Izacard and Edouard Grave. 2020. [Leveraging passage retrieval with generative models for open domain question answering](https://md.research.elamur.ai/free/en/papers/izacard-2021-leveraging-passage-retrieval-generative-models/). ArXiv, abs/2007.01282.
20. Ziwei Ji, Nayeon Lee, and Rita Frieske et al. 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.
21. Chaoya Jiang, Haiyang Xu, Mengfan Dong, Jiaxing Chen, Wei Ye, Mingshi Yan, Qinghao Ye, Ji Zhang, Fei Huang, and Shikun Zhang. 2023. Hallucination augmented contrastive learning for multimodal large language model. ArXiv, abs/2312.06968. — [arXiv:2312.06968](https://arxiv.org/abs/2312.06968) [find](https://scholar.google.com/scholar?q=Chaoya+Jiang%2C+Haiyang+Xu%2C+Mengfan+Dong%2C+Jiaxing+Chen%2C+Wei+Ye%2C+Mingshi+Yan%2C+Qinghao+Ye%2C+Ji+Zhang%2C+Fei+Huang%2C+and+Shikun+Zhang%2E+2023%2E+Hallucination+augmented+contrastive+learning+for+multimodal+large+la)
22. Haoqiang Kang, Juntong Ni, and Huaxiu Yao. 2023. Ever: Mitigating hallucination in large language models through real-time verification and rectification. CoRR, abs/2311.09114. — [arXiv:2311.09114](https://arxiv.org/abs/2311.09114) [find](https://scholar.google.com/scholar?q=Haoqiang+Kang%2C+Juntong+Ni%2C+and+Huaxiu+Yao%2E+2023%2E+Ever%3A+Mitigating+hallucination+in+large+language+models+through+real%2Dtime+verification+and+rectification%2E+CoRR%2C+abs%2F2311%2E09114%2E+%E2%80%94+%5BarXiv%3A2311%2E09114%5D%28ht)
23. Vladimir Karpukhin, Barlas Oğuz, and Sewon Min et al. 2020. [Dense passage retrieval for open-domain question answering](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/). ArXiv, abs/2004.04906.
24. Abdullatif Köksal, Renat Aksitov, and Chung-Ching Chang. 2023. Hallucination augmented recitations for language models. arXiv preprint arXiv:2311.07424. — [arXiv:2311.07424](https://arxiv.org/abs/2311.07424) [find](https://scholar.google.com/scholar?q=Abdullatif+K%C3%B6ksal%2C+Renat+Aksitov%2C+and+Chung%2DChing+Chang%2E+2023%2E+Hallucination+augmented+recitations+for+language+models%2E+arXiv+preprint+arXiv%3A2311%2E07424%2E+%E2%80%94+%5BarXiv%3A2311%2E07424%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2311)
25. Junyi Li and Xiaoxue Cheng et al. 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/). CoRR, abs/2305.11747.
26. Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji rong Wen. 2023. [Evaluating object hallucination in large vision-language models](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-object-hallucination-large-vision/). In Conference on Empirical Methods in Natural Language Processing.
27. Yuanzhi Li and Sébastien Bubeck et al. 2023b. [Textbooks are all you need II: phi-1.5 technical report](https://md.research.elamur.ai/free/en/papers/li-2023-textbooks-all-need-ii-phi/). CoRR, abs/2309.05463.
28. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. [Truthfulqa: Measuring how models mimic human falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/). In ACL, pages 3214–3252.
29. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. [Microsoft coco: Common objects in context](https://md.research.elamur.ai/free/en/papers/lin-2014-microsoft-coco-common-objects-context/). In European Conference on Computer Vision.
30. Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. 2023. [Mitigating hallucination in large multi-modal models via robust instruction tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-mitigating-hallucination-large-multi-modal/).
31. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. Locating and editing factual associations in gpt. In Neural Information Processing Systems. [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+Neural+Information+Processing+Systems%2E)
32. Sewon Min and Kalpesh Krishna et al. 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/). CoRR, abs/2305.14251.
33. Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D. Manning. 2021. Fast model editing at scale. ArXiv, abs/2110.11309. [find](https://scholar.google.com/scholar?q=Eric+Mitchell%2C+Charles+Lin%2C+Antoine+Bosselut%2C+Chelsea+Finn%2C+and+Christopher+D%2E+Manning%2E+2021%2E+Fast+model+editing+at+scale%2E+ArXiv%2C+abs%2F2110%2E11309%2E)
34. Dor Muhlgay, Ori Ram, and Inbal Magar et al. 2023. [Generating benchmarks for factuality evaluation of language models](https://md.research.elamur.ai/free/en/papers/muhlgay-2024-generating-benchmarks-factuality-evaluation-language/). CoRR, abs/2307.06908.
35. Reiichiro Nakano, Jacob Hilton, and et al. 2021. [Webgpt: Browser-assisted question-answering with human feedback](https://md.research.elamur.ai/free/en/papers/nakano-2021-webgpt-browser-assisted-question-answering/). ArXiv, abs/2112.09332.
36. Long Ouyang, Jeff Wu, and Xu Jiang et al. 2022. [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/). ArXiv, abs/2203.02155.
37. Nayeon Lee, Wei Ping, and Peng et al. Xu. 2022. [Factuality enhanced language models for open-ended text generation](https://md.research.elamur.ai/free/en/papers/lee-2022-factuality-enhanced-language-models-open/). NeuralPS, 35:34586–34599.
38. Rafael Rafailov, Archit Sharma, and et al. 2023. [Direct preference optimization: Your language model is secretly a reward model](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/). CoRR, abs/2305.18290.
39. Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Li Bing. 2023. [Mitigating object hallucinations in large vision-language models through visual contrastive decoding](https://md.research.elamur.ai/free/en/papers/leng-2023-mitigating-object-hallucinations-large-vision/). ArXiv, abs/2311.16922.
40. Vipula Rawte, Swagata Chakraborty, and Agnibh et al. Pathak. 2023a. [The troubling emergence of hallucination in large language models - an extensive definition, quantification, and prescriptive remediations](https://md.research.elamur.ai/free/en/papers/rawte-2023-troubling-emergence-hallucination-large-language/). In EMNLP 2023, pages 2541–2573.
41. Patrick Lewis, Ethan Perez, and et al. 2020. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). ArXiv, abs/2005.11401.
42. Vipula Rawte, Amit P. Sheth, and Amitava Das. 2023b. [A survey of hallucination in large foundation models](https://md.research.elamur.ai/free/en/papers/rawte-2023-survey-hallucination-large-foundation-models/). CoRR, abs/2309.05922.
43. Evgeniia Razumovskaia, Ivan Vulic, and Pavle Markovic et al. 2023. Dial beinfo for faithfulness: Improving factuality of information-seeking dialogue via behavioural fine-tuning. CoRR, abs/2311.09800. [find](https://scholar.google.com/scholar?q=Evgeniia+Razumovskaia%2C+Ivan+Vulic%2C+and+Pavle+Markovic+et+al%2E+2023%2E+Dial+beinfo+for+faithfulness%3A+Improving+factuality+of+information%2Dseeking+dialogue+via+behavioural+fine%2Dtuning%2E+CoRR%2C+abs%2F2311%2E09800%2E)
44. Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018. [Object hallucination in image captioning](https://md.research.elamur.ai/free/en/papers/rohrbach-2018-object-hallucination-image-captioning/). In Conference on Empirical Methods in Natural Language Processing.
45. Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, and Amanda Askell et al. 2023. [Towards understanding sycophancy in language models](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/). CoRR, abs/2310.13548.
46. Weijia Shi, Xiaochuang Han, and et al. 2023. [Trusting your evidence: Hallucinate less with context-aware decoding](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/). arXiv preprint arXiv:2305.14739.
47. Noah Shinn, Federico Cassano, and Gopinath et al. 2023. [Reflexion: Language agents with verbal reinforcement learning](https://md.research.elamur.ai/free/en/papers/shinn-2023-reflexion-language-agents-verbal-reinforcement/). In NeuralPS.
48. Lichao Sun, Yue Huang, and Haoran Wang et al. 2024. [Trustllm: Trustworthiness in large language models](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/). ArXiv, abs/2401.05561.
49. Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liangyan Gui, Yu-Xiong Wang, Yiming Yang, Kurt Keutzer, and Trevor Darrell. 2023. [Aligning large multimodal models with factually augmented rlhf](https://md.research.elamur.ai/free/en/papers/sun-2023-aligning-large-multimodal-models-factually/). ArXiv, abs/2309.14525.
50. James Thorne, Andreas Vlachos, and et al. 2018. [FEVER: a large-scale dataset for fact extraction and VERification](https://md.research.elamur.ai/free/en/papers/thorne-2018-fever-large-scale-dataset-fact/). In NAACL, pages 809–819.
51. Katherine Tian, Eric Mitchell, and et al. 2023. [Fine-tuning language models for factuality](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/). arXiv preprint arXiv:2311.08401.
52. S. M. Towhidul Islam Tonmoy, S. M. Mehedi Zaman, and et al. 2024. [A comprehensive survey of hallucination mitigation techniques in large language models](https://md.research.elamur.ai/free/en/papers/tonmoy-2024-comprehensive-survey-hallucination-mitigation-techniques/). CoRR, abs/2401.01313.
53. Faraz Torabi, Garrett Warnell, and Peter Stone. 2018. Behavioral cloning from observation. In IJCAI, pages 4950–4957. ijcai.org. [find](https://scholar.google.com/scholar?q=Faraz+Torabi%2C+Garrett+Warnell%2C+and+Peter+Stone%2E+2018%2E+Behavioral+cloning+from+observation%2E+In+IJCAI%2C+pages+4950%E2%80%934957%2E+ijcai%2Eorg%2E)
54. Hugo Touvron, Louis Martin, and et al. 2023. [Llama 2: Open foundation and fine-tuned chat models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/). CoRR, abs/2307.09288.
55. Neeraj Varshney, Wenlin Yao, and et al. 2023. [A stitch in time saves nine: Detecting and mitigating hallucinations of llms by validating low-confidence generation](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/). CoRR, abs/2307.03987.
56. Tu Vu, Mohit Iyyer, and et al. 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/). arXiv preprint arXiv:2310.03214.
57. Boxin Wang, Wei Ping, and et al. 2023a. Shall we pre-train autoregressive language models with retrieval? a comprehensive study. In EMNLP. [find](https://scholar.google.com/scholar?q=Boxin+Wang%2C+Wei+Ping%2C+and+et+al%2E+2023a%2E+Shall+we+pre%2Dtrain+autoregressive+language+models+with+retrieval%3F+a+comprehensive+study%2E+In+EMNLP%2E)
58. Cunxiang Wang, Xiaoze Liu, and et al. 2023b. [Survey on factuality in large language models: Knowledge, retrieval and domain-specificity](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/). ArXiv, abs/2310.07521.
59. Yuxia Wang, Revanth Gangi Reddy, and et al. 2023c. Factcheck-gpt: End-to-end fine-grained document-level fact-checking and correction of LLM output. CoRR, abs/2311.09000. — [arXiv:2311.09000](https://arxiv.org/abs/2311.09000) [find](https://scholar.google.com/scholar?q=Yuxia+Wang%2C+Revanth+Gangi+Reddy%2C+and+et+al%2E+2023c%2E+Factcheck%2Dgpt%3A+End%2Dto%2Dend+fine%2Dgrained+document%2Dlevel+fact%2Dchecking+and+correction+of+LLM+output%2E+CoRR%2C+abs%2F2311%2E09000%2E+%E2%80%94+%5BarXiv%3A2311%2E09000%5D%28https%3A%2F%2F)
60. Jason Wei, Xuezhi Wang, and Dale et al. 2022. [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 NeurIPS 2022.
61. Jerry W. Wei, Da Huang, and Yifeng Lu et al. 2023. [Simple synthetic data reduces sycophancy in large language models](https://md.research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/). CoRR, abs/2308.03958.
62. Zhilin Yang and Peng Qi et al. 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 EMNLP 2018, pages 2369–2380.
63. Shunyu Yao, Jeffrey Zhao, and Dian et al. 2023. [React: Synergizing reasoning and acting in language models](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/). In ICLR.
64. Shukang Yin, Chaoyou Fu, and et al. 2023a. [Woodpecker: Hallucination correction for multimodal large language models](https://md.research.elamur.ai/free/en/papers/yin-2023-woodpecker-hallucination-correction-multimodal-large/). CoRR, abs/2310.16045.
65. Zhangyue Yin, Qiushi Sun, and Qipeng Guo et al. 2023b. Do large language models know what they don’t know? In ACL, pages 8653–8665. [find](https://scholar.google.com/scholar?q=Zhangyue+Yin%2C+Qiushi+Sun%2C+and+Qipeng+Guo+et+al%2E+2023b%2E+Do+large+language+models+know+what+they+don%E2%80%99t+know%3F+In+ACL%2C+pages+8653%E2%80%938665%2E)
66. Bohan Zhai, Shijia Yang, Xiangchen Zhao, Chenfeng Xu, Sheng Shen, Dongdi Zhao, Kurt Keutzer, Manling Li, Tan Yan, and Xiangjun Fan. 2023. Halle-switch: Rethinking and controlling object existence hallucinations in large vision language models for detailed caption. ArXiv, abs/2310.01779. — [arXiv:2310.01779](https://arxiv.org/abs/2310.01779) [find](https://scholar.google.com/scholar?q=Bohan+Zhai%2C+Shijia+Yang%2C+Xiangchen+Zhao%2C+Chenfeng+Xu%2C+Sheng+Shen%2C+Dongdi+Zhao%2C+Kurt+Keutzer%2C+Manling+Li%2C+Tan+Yan%2C+and+Xiangjun+Fan%2E+2023%2E+Halle%2Dswitch%3A+Rethinking+and+controlling+object+existence+hall)
67. Hanning Zhang, Shizhe Diao, and et al. 2023a. [R-tuning: Teaching large language models to refuse unknown questions](https://md.research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/). CoRR, abs/2311.09677.
68. Muru Zhang, Ofir Press, and et al. 2023b. [How language model hallucinations can snowball](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/). CoRR, abs/2305.13534.
69. Yue Zhang, Yafu Li, and et al. 2023c. [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/). CoRR, abs/2309.01219.
70. Ce Zheng, Lei Li, and et al. 2023. Can we edit factual knowledge by in-context learning? In EMNLP, pages 4862–4876. [find](https://scholar.google.com/scholar?q=Ce+Zheng%2C+Lei+Li%2C+and+et+al%2E+2023%2E+Can+we+edit+factual+knowledge+by+in%2Dcontext+learning%3F+In+EMNLP%2C+pages+4862%E2%80%934876%2E)
71. Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, and Huaxiu Yao. 2023. [Analyzing and mitigating object hallucination in large vision-language models](https://md.research.elamur.ai/free/en/papers/zhou-2023-analyzing-mitigating-object-hallucination-large/). ArXiv, abs/2310.00754.

*Updated 2 November 2026*