🔗 Who cites «Factuality Enhanced Language Models for Open-Ended Text Generation»

110 papers of our library cite it (of the 149,439 whose bibliographies we read) · 34 with a page here, the most cited first · ← back to «Factuality Enhanced Language Models for Open-Ended Text Generation»

1. Survey of Hallucination in Natural Language GenerationACM Computing SurveysZiwei Ji, Nayeon Lee, Rita Frieske, et al.2022 · 5,974 citations
2. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open QuestionsACM Transactions on Information SystemsLei Huang, Weijiang Yu, Weitao Ma, et al.2023 · 3,231 citations
3. FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text GenerationConference on Empirical Methods in Natural Language Processing (EMNLP)Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al.2023 · 987 citations
4. Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language ModelsComputational LinguisticsYue Zhang, Yafu Li, Leyang Cui, et al.2023 · 977 citations
5. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and InteractivityarXiv:2302.04023Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.2023 · 882 citations
6. Improving Factuality and Reasoning in Language Models through Multiagent DebatearXiv:2305.14325Yilun Du, Shuang Li, Antonio Torralba, et al.2023 · 587 citations
7. Large Legal Fictions: Profiling Legal Hallucinations in Large Language ModelsarXiv:2401.01301 · Journal of Legal Analysis 16, no. 1 (2024): 64-93Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.2024 · 437 citations
8. CRITIC: Large Language Models Can Self-Correct with Tool-Interactive CritiquingarXiv:2305.11738Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.2023 · 326 citations
9. Hallucination is Inevitable: An Innate Limitation of Large Language ModelsarXiv:2401.11817Ziwei Xu, Sanjay Jain, Mohan Kankanhalli2024 · 312 citations
10. Challenges and Applications of Large Language ModelsarXiv:2307.10169Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 301 citations
11. BlenderBot 3: a deployed conversational agent that continually learns to responsibly engagearXiv:2208.03188Kurt Shuster, Jing Xu, Mojtaba Komeili, et al.2022 · 241 citations
12. OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-AllocationIEEE Computer Society Conference on Computer Vision and Pattern RecognitionQidong Huang, Xiaoyi Dong, Pan Zhang, et al.2023 · 238 citations
13. On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A SurveyFindings of the Association for Computational LinguisticsLin Long, Rui Wang, Ruixuan Xiao, et al.2024 · 237 citations
14. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
15. Combating Misinformation in the Age of LLMs: Opportunities and ChallengesAI MagazineCanyu Chen, Kai Shu2023 · 214 citations
16. Knowledge Conflicts for LLMs: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.2024 · 182 citations
17. Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityarXiv:2310.07521Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.2023 · 171 citations
18. The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive RemediationsConference on Empirical Methods in Natural Language Processing (EMNLP)Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, et al.2023 · 171 citations
19. The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Junyi Li, Jie Chen, Ruiyang Ren, et al.2024 · 153 citations
20. Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and MitigationarXiv:2305.15852Niels Mundler, Jingxuan He, Slobodan Jenko, et al.2023 · 152 citations
21. A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence GenerationarXiv:2307.03987Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu2023 · 143 citations
22. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
23. R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Hanning Zhang, Shizhe Diao, Yong Lin, et al.2024 · 99 citations
24. Alignment for HonestyConference on Neural Information Processing Systems (NeurIPS)Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.2023 · 90 citations
25. Generating Benchmarks for Factuality Evaluation of Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Dor Muhlgay, Ori Ram, Inbal Magar, et al.2024 · 81 citations
26. Factuality of Large Language Models: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, et al.2024 · 77 citations
27. Resolving Knowledge Conflicts in Large Language ModelsarXiv:2310.00935Yike Wang, Shangbin Feng, Heng Wang, et al.2023 · 48 citations
28. Unfamiliar Finetuning Examples Control How Language Models HallucinateConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Katie Kang, Eric Wallace, Claire Tomlin, et al.2025 · 47 citations
29. “According to . . . “: Prompting Language Models Improves Quoting from Pre-Training DataConference of the European Chapter of the Association for Computational Linguistics (EACL)Orion Weller, Marc Marone, Nathaniel Weir, et al.2024 · 46 citations
30. Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Yuan Sui, Yufei He, Zifeng Ding, et al.2025 · 16 citations
31. Evaluating Large Language Models for Health-related Queries with PresuppositionsFindings of the Association for Computational LinguisticsNavreet Kaur, Monojit Choudhury, Danish Pruthi2024 · 15 citations
32. ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error AnnotationsConference of the European Chapter of the Association for Computational Linguistics (EACL)Yindong Wang, Martin Preiß, Margarita Bugueño, et al.2026
33. What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM PerformanceFindings of the Association for Computational LinguisticsWilliam Watson, Nicole Cho, Sumitra Ganesh, et al.2026
34. Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)Journal of SoftwareZeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.2024

76 more papers of our library cite it; they have no page here yet.

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/lee-2022-factuality-enhanced-language-models-open/cited-by/