🧑‍🔬 Who cites I-Chun Chern

222 citations of their works from 192 papers of our library (of the 149,439 whose bibliographies we read) · 63 with a page here, the most cited first · ← I-Chun Chern

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. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
11. Combating Misinformation in the Age of LLMs: Opportunities and ChallengesAI MagazineCanyu Chen, Kai Shu2023 · 214 citations
12. 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
13. Tool Learning with Large Language Models: A SurveyFrontiers of Computer ScienceChangle Qu, Sunhao Dai, Xiaochi Wei, et al.2024 · 195 citations
14. 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
15. Evaluating Large Language Models: A Comprehensive SurveyarXiv:2310.19736Zishan Guo, Renren Jin, Chuang Liu, et al.2023 · 182 citations
16. Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityarXiv:2310.07521Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.2023 · 171 citations
17. Long-form factuality in large language modelsConference on Neural Information Processing Systems (NeurIPS)Jerry Wei, Chengrun Yang, Xinying Song, et al.2024 · 165 citations
18. 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
19. Measuring short-form factuality in large language modelsarXiv:2411.04368Jason Wei, Nguyen Karina, Hyung Won Chung, et al.2024 · 160 citations
20. 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
21. 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
22. Fine-tuning Language Models for FactualityarXiv:2311.08401Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.2023 · 127 citations
23. Fine-grained Hallucination Detection and Editing for Language ModelsarXiv:2401.06855Abhika Mishra, Akari Asai, Vidhisha Balachandran, et al.2024 · 100 citations
24. 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
25. Alignment for HonestyConference on Neural Information Processing Systems (NeurIPS)Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.2023 · 90 citations
26. 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
27. A Survey of Large Language Models in Medicine: Progress, Application, and ChallengearXiv:2311.05112Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.2023 · 87 citations
28. HalluLens: LLM Hallucination BenchmarkAnnual Meeting of the Association for Computational Linguistics (ACL)Yejin Bang, Ziwei Ji, Alan Schelten, et al.2025 · 82 citations
29. 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
30. 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
31. 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
32. 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
33. Introducing v0.5 of the AI Safety Benchmark from MLCommonsarXiv:2404.12241Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.2024 · 38 citations
34. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language ModelsarXiv:2411.07140Yancheng He, Shilong Li, Jiaheng Liu, et al.2024 · 34 citations
35. The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form InputarXiv:2501.03200Alon Jacovi, Andrew Wang, Chris Alberti, et al.2025 · 32 citations
36. Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and EvaluationACM Computing SurveysSteffen Eger, Yong Cao, Jennifer D'Souza, et al.2025 · 25 citations
37. 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
38. Towards Effective Extraction and Evaluation of Factual ClaimsAnnual Meeting of the Association for Computational Linguistics (ACL)Dasha Metropolitansky, Jonathan Larson2025 · 20 citations
39. Comparing Hallucination Detection Metrics for Multilingual GenerationarXiv:2402.10496Haoqiang Kang, Terra Blevins, Luke Zettlemoyer2024 · 18 citations
40. FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Farima Fatahi Bayat, Lechen Zhang, Sheza Munir, et al.2025 · 16 citations
41. Evaluating Large Language Models for Health-related Queries with PresuppositionsFindings of the Association for Computational LinguisticsNavreet Kaur, Monojit Choudhury, Danish Pruthi2024 · 15 citations
42. Halwasa: Quantify and Analyze Hallucinations in Large Language Models: Arabic as a Case StudyLanguage Resources and Evaluation Conference (LREC)Hamdy Mubarak, Hend Al-Khalifa, Khaloud Suliman Alkhalefah2024 · 14 citations
43. U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMsarXiv:2412.03205Konstantin Chernyshev, Vitaliy Polshkov, Ekaterina Artemova, et al.2024 · 13 citations
44. 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
45. SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric KnowledgearXiv:2509.07968Lukas Haas, Gal Yona, Giovanni D'Antonio, et al.2025 · 12 citations
46. Halu-J: Critique-Based Hallucination JudgearXiv:2407.12943Binjie Wang, Steffi Chern, Ethan Chern, et al.2024 · 8 citations
47. 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
48. 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
49. Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language ModelsarXiv:2412.15265Yingshui Tan, Boren Zheng, Baihui Zheng, et al.2024 · 4 citations
50. HalluTruthQA: A Fine-Grained Benchmark for Hallucination Detection, Localization, and Explanation in Arabic Question AnsweringarXiv:2607.20219Abdessalam Bouchekif; Mohammed-En-Nadhir Zighem; Salah Eddine Bekhouche; Hichem Telli; Somaya Eltanbouly; Shahd Gaben; Heba Sbahi; Samer Rashwani; Mutaz Al-Khatib; Emad Mohamed; Mohammed Ghaly;, Abdenour Hadid2026 · 3 citations
51. HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination TaxonomyAnnual Meeting of the Association for Computational Linguistics (ACL)Fan Xu, Xinyu Hu, Zhenghan Yu, et al.2026 · 1 citation
52. HalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth VerificationarXiv:2608.03966Salah Eddine Bekhouche; Abdessalam Bouchekif; Hichem Telli; Mohammed-En-Nadhir Zighem;, Abdenour Hadid2026 · 1 citation
53. Assessing Web Search Credibility and Response Groundedness in Chat AssistantsConference of the European Chapter of the Association for Computational Linguistics (EACL)Ivan Vykopal, Matúš Pikuliak, Simon Ostermann, et al.2026
54. Detecting Hallucinations in Authentic LLM–Human InteractionsLanguage Resources and Evaluation Conference (LREC)Yujie Ren, Niklas Gruhlke, Anne Lauscher2026
55. Distill and Align Decomposition for Enhanced Claim VerificationFindings of the Association for Computational LinguisticsJabez Magomere, Elena Kochkina, Samuel Mensah, et al.2026
56. FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMsFindings of the Association for Computational LinguisticsAlbert Sawczyn, Jakub Binkowski, Denis Janiak, et al.2026
57. 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
58. 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
59. Learning to Control Summaries with Score RankingFindings of the Association for Computational LinguisticsHongye Liu, Liang Ding, Ricardo Henao2026
60. 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
61. PRISM: Probing Reasoning, Instruction, and Source Memory in LLM HallucinationsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuhe Wu, Guangyu Wang, Yuran Chen, et al.2026
62. 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
63. 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

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