🧑‍🔬 Who cites Kehua Feng

168 citations of their works from 168 papers of our library (of the 149,439 whose bibliographies we read) · 51 with a page here, the most cited first · ← Kehua Feng

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. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
4. Qwen Technical ReportarXiv:2309.16609Jinze Bai, Shuai Bai, Yunfei Chu, et al.2023 · 1,669 citations
5. 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
6. 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
7. Security and Privacy Challenges of Large Language Models: A SurveyACM Computing SurveysBadhan Chandra Das, M. Hadi Amini, Yanzhao Wu2024 · 424 citations
8. Chain-of-Verification Reduces Hallucination in Large Language ModelsarXiv:2309.11495Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, et al.2023 · 305 citations
9. A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future DirectionsComputational LinguisticsJunchao Wu, Shu Yang, Runzhe Zhan, et al.2025 · 277 citations
10. Combating Misinformation in the Age of LLMs: Opportunities and ChallengesAI MagazineCanyu Chen, Kai Shu2023 · 214 citations
11. RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Cheng Niu, Yuanhao Wu, Juno Zhu, et al.2024 · 207 citations
12. Tool Learning with Large Language Models: A SurveyFrontiers of Computer ScienceChangle Qu, Sunhao Dai, Xiaochi Wei, et al.2024 · 195 citations
13. 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
14. Evaluating Large Language Models: A Comprehensive SurveyarXiv:2310.19736Zishan Guo, Renren Jin, Chuang Liu, et al.2023 · 182 citations
15. Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityarXiv:2310.07521Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.2023 · 171 citations
16. Long-form factuality in large language modelsConference on Neural Information Processing Systems (NeurIPS)Jerry Wei, Chengrun Yang, Xinying Song, et al.2024 · 165 citations
17. When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMsTransactions of the Association for Computational Linguistics (TACL)Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.2024 · 161 citations
18. 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
19. Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction StrategiesTransactions of the Association for Computational Linguistics (TACL)Liangming Pan, Michael Saxon, Wenda Xu, et al.2024 · 140 citations
20. Fine-tuning Language Models for FactualityarXiv:2311.08401Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.2023 · 127 citations
21. Fine-grained Hallucination Detection and Editing for Language ModelsarXiv:2401.06855Abhika Mishra, Akari Asai, Vidhisha Balachandran, et al.2024 · 100 citations
22. Fact-Checking the Output of Large Language Models via Token-Level Uncertainty QuantificationFindings of the Association for Computational LinguisticsEkaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, et al.2024 · 96 citations
23. Alignment for HonestyConference on Neural Information Processing Systems (NeurIPS)Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.2023 · 90 citations
24. Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language ModelsFindings of the Association for Computational LinguisticsMiaoran Li, Baolin Peng, Michel Galley, et al.2024 · 87 citations
25. HalluLens: LLM Hallucination BenchmarkAnnual Meeting of the Association for Computational Linguistics (ACL)Yejin Bang, Ziwei Ji, Alan Schelten, et al.2025 · 82 citations
26. Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention MapsConference on Empirical Methods in Natural Language Processing (EMNLP)Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, et al.2024 · 78 citations
27. 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
28. Reinforcement Learning via Self-DistillationarXiv:2601.20802Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.2026 · 74 citations
29. VeriScore: Evaluating the factuality of verifiable claims in long-form text generationFindings of the Association for Computational LinguisticsYixiao Song, Yekyung Kim, Mohit Iyyer2024 · 72 citations
30. A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning ChainsAnnual Meeting of the Association for Computational Linguistics (ACL)Alon Jacovi, Yonatan Bitton, Bernd Bohnet, et al.2024 · 54 citations
31. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language ModelsarXiv:2411.07140Yancheng He, Shilong Li, Jiaheng Liu, et al.2024 · 34 citations
32. SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific LiteratureConference on Empirical Methods in Natural Language Processing (EMNLP)David Wadden, Kejian Shi, Jacob Morrison, et al.2025 · 27 citations
33. HALoGEN: Fantastic LLM Hallucinations and Where to Find ThemAnnual Meeting of the Association for Computational Linguistics (ACL)Abhilasha Ravichander, Shrusti Ghela, David Wadden, et al.2025 · 22 citations
34. AI4Research: A Survey of Artificial Intelligence for Scientific ResearchScience China Information SciencesQiguang Chen, Mingda Yang, Libo Qin, et al.2025 · 21 citations
35. Towards Effective Extraction and Evaluation of Factual ClaimsAnnual Meeting of the Association for Computational Linguistics (ACL)Dasha Metropolitansky, Jonathan Larson2025 · 20 citations
36. Comparing Hallucination Detection Metrics for Multilingual GenerationarXiv:2402.10496Haoqiang Kang, Terra Blevins, Luke Zettlemoyer2024 · 18 citations
37. ChemAgent: Self-updating Library in Large Language Models Improves Chemical ReasoningarXiv:2501.06590Xiangru Tang, Tianyu Hu, Muyang Ye, et al.2025 · 15 citations
38. Evaluating Large Language Models for Health-related Queries with PresuppositionsFindings of the Association for Computational LinguisticsNavreet Kaur, Monojit Choudhury, Danish Pruthi2024 · 15 citations
39. A Probabilistic Framework for LLM Hallucination Detection via Belief Tree PropagationConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Bairu Hou, Yang Zhang, Jacob Andreas, et al.2025 · 12 citations
40. Halu-J: Critique-Based Hallucination JudgearXiv:2407.12943Binjie Wang, Steffi Chern, Ethan Chern, et al.2024 · 8 citations
41. Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory RetrievalFindings of the Association for Computational LinguisticsAditya Sharma, Christopher Pal, Amal Zouaq2026 · 5 citations
42. SciClaims: An End-to-End Generative System for Biomedical Claim AnalysisConference on Empirical Methods in Natural Language Processing (EMNLP)Raul Ortega, Jose Manuel Gomez-Perez2025 · 4 citations
43. Instructions are all you need: Self-supervised Reinforcement Learning for Instruction FollowingAnnual Meeting of the Association for Computational Linguistics (ACL)Qingyu Ren, Qianyu He, Powei Chang, et al.2026 · 3 citations
44. Datasets for Scientific Literature Understanding: A SurveyFindings of the Association for Computational LinguisticsYuanzhe Zhang, Xun Zhao, Maodi Hu, et al.2026
45. FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMsFindings of the Association for Computational LinguisticsAlbert Sawczyn, Jakub Binkowski, Denis Janiak, et al.2026
46. From Conversation to Evaluation: Benchmarking LLMs on Development Knowledge via SimpleDevQAFindings of the Association for Computational LinguisticsJing Zhang, Lianghong Guo, Yanlin Wang, et al.2026
47. JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query GenerationFindings of the Association for Computational LinguisticsFan Xu, Huixuan Zhang, Zhenliang Zhang, et al.2026
48. JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM EvaluationFindings of the Association for Computational LinguisticsZhichao Shi, Xuhui Jiang, Chengjin Xu, et al.2026
49. KGRxn-LLM: Knowledge Graph Enhanced Large Language Models for Molecular Reaction ReasoningBioNLPWeichen Liu, Qiyao Xue, Yuyang Wu, et al.2026
50. Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-JudgmentsAnnual Meeting of the Association for Computational Linguistics (ACL)Hao Mi, Qiang Sheng, Shaofei Wang, et al.2026
51. WildSci: Advancing Scientific Reasoning from In-the-Wild LiteratureFindings of the Association for Computational LinguisticsTengxiao Liu, Deepak Nathani, Zekun Li, et al.2026

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