---
title: "Who cites Shiqi Chen — 186 papers of our library"
description: "186 papers of the Elamur Research library cite the works of Shiqi Chen, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/shiqi-chen/cited-by/"
---
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# Who cites Shiqi Chen

214 citations of their works from 186 papers of our library (of the 149,439 whose bibliographies we read) · 61 with a page here, the most cited first · [← Shiqi Chen](https://md.research.elamur.ai/free/en/authors/shiqi-chen/)

1. **[Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ziwei 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 Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
3. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
4. [**Qwen Technical Report arXiv:2309.16609**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,669 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
5. **[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](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations*
6. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models arXiv:2401.01301 · Journal of Legal Analysis 16, no. 1 (2024): 64-93**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 437 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
7. **[Security and Privacy Challenges of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 424 citations*
8. [**Chain-of-Verification Reduces Hallucination in Large Language Models arXiv:2309.11495**Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/dhuliawala-2023-chain-verification-reduces-hallucination-large/)
9. **[A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://md.research.elamur.ai/free/en/papers/wu-2025-survey-llm-generated-text-detection/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Junchao Wu, Shu Yang, Runzhe Zhan, et al.*2025 · 277 citations*
10. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
11. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
12. **[RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 207 citations*
13. **[Tool Learning with Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/qu-2024-tool-learning-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Changle Qu, Sunhao Dai, Xiaochi Wei, et al.*2024 · 195 citations*
14. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
15. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
16. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
17. **[Long-form factuality in large language models](https://md.research.elamur.ai/free/en/papers/wei-2024-long-form-factuality-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**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 LLMs](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.*2024 · 161 citations*
19. [**Measuring short-form factuality in large language models arXiv:2411.04368**Jason Wei, Nguyen Karina, Hyung Won Chung, et al.*2024 · 160 citations*](https://md.research.elamur.ai/free/en/papers/wei-2024-measuring-short-form-factuality-large/)
20. **[The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-dawn-after-dark-empirical-study/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Strategies](https://md.research.elamur.ai/free/en/papers/pan-2024-automatically-correcting-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Liangming Pan, Michael Saxon, Wenda Xu, et al.*2024 · 140 citations*
22. [**Fine-tuning Language Models for Factuality arXiv:2311.08401**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
23. [**Fine-grained Hallucination Detection and Editing for Language Models arXiv:2401.06855**Abhika Mishra, Akari Asai, Vidhisha Balachandran, et al.*2024 · 100 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2024-fine-grained-hallucination-detection-editing/)
24. **[Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification](https://md.research.elamur.ai/free/en/papers/fadeeva-2024-fact-checking-output-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, et al.*2024 · 96 citations*
25. **[Alignment for Honesty](https://md.research.elamur.ai/free/en/papers/yang-2023-alignment-honesty/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.*2023 · 90 citations*
26. **[Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-self-checker-plug-play-modules/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Miaoran Li, Baolin Peng, Michel Galley, et al.*2024 · 87 citations*
27. **[HalluLens: LLM Hallucination Benchmark](https://md.research.elamur.ai/free/en/papers/bang-2025-hallulens-llm-hallucination-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yejin Bang, Ziwei Ji, Alan Schelten, et al.*2025 · 82 citations*
28. **[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/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, et al.*2024 · 78 citations*
29. **[Factuality of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2024-factuality-large-language-models-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, et al.*2024 · 77 citations*
30. **[VeriScore: Evaluating the factuality of verifiable claims in long-form text generation](https://md.research.elamur.ai/free/en/papers/song-2024-veriscore-evaluating-factuality-verifiable-claims/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixiao Song, Yekyung Kim, Mohit Iyyer*2024 · 72 citations*
31. **[A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains](https://md.research.elamur.ai/free/en/papers/jacovi-2024-chain-thought-is-as-strong/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alon Jacovi, Yonatan Bitton, Bernd Bohnet, et al.*2024 · 54 citations*
32. [**Introducing v0.5 of the AI Safety Benchmark from MLCommons arXiv:2404.12241**Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2024-introducing-v0-5-ai-safety/)
33. [**Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models arXiv:2411.07140**Yancheng He, Shilong Li, Jiaheng Liu, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-chinese-simpleqa-chinese-factuality/)
34. [**The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input arXiv:2501.03200**Alon Jacovi, Andrew Wang, Chris Alberti, et al.*2025 · 32 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2025-facts-grounding-leaderboard-benchmarking-llms/)
35. **[Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://md.research.elamur.ai/free/en/papers/eger-2025-transforming-science-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Steffen Eger, Yong Cao, Jennifer D'Souza, et al.*2025 · 25 citations*
36. **[HALoGEN: Fantastic LLM Hallucinations and Where to Find Them](https://md.research.elamur.ai/free/en/papers/ravichander-2025-halogen-fantastic-llm-hallucinations-where/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abhilasha Ravichander, Shrusti Ghela, David Wadden, et al.*2025 · 22 citations*
37. **[Towards Effective Extraction and Evaluation of Factual Claims](https://md.research.elamur.ai/free/en/papers/metropolitansky-2025-effective-extraction-evaluation-factual-claims/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dasha Metropolitansky, Jonathan Larson*2025 · 20 citations*
38. [**Comparing Hallucination Detection Metrics for Multilingual Generation arXiv:2402.10496**Haoqiang Kang, Terra Blevins, Luke Zettlemoyer*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/kang-2024-comparing-hallucination-detection-metrics-multilingual/)
39. **[FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation](https://md.research.elamur.ai/free/en/papers/bayat-2025-factbench-dynamic-benchmark-wild-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Farima Fatahi Bayat, Lechen Zhang, Sheza Munir, et al.*2025 · 16 citations*
40. **[Evaluating Large Language Models for Health-related Queries with Presuppositions](https://md.research.elamur.ai/free/en/papers/kaur-2024-evaluating-large-language-models-health/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Navreet Kaur, Monojit Choudhury, Danish Pruthi*2024 · 15 citations*
41. **[Halwasa: Quantify and Analyze Hallucinations in Large Language Models: Arabic as a Case Study](https://md.research.elamur.ai/free/en/papers/mubarak-2024-halwasa-quantify-analyze-hallucinations-large/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hamdy Mubarak, Hend Al-Khalifa, Khaloud Suliman Alkhalefah*2024 · 14 citations*
42. [**U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs arXiv:2412.03205**Konstantin Chernyshev, Vitaliy Polshkov, Ekaterina Artemova, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/chernyshev-2024-u-math-university-level-benchmark/)
43. **[A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation](https://md.research.elamur.ai/free/en/papers/hou-2025-probabilistic-framework-llm-hallucination-detection/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bairu Hou, Yang Zhang, Jacob Andreas, et al.*2025 · 12 citations*
44. [**SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge arXiv:2509.07968**Lukas Haas, Gal Yona, Giovanni D'Antonio, et al.*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/haas-2025-simpleqa-verified-reliable-factuality-benchmark/)
45. [**Halu-J: Critique-Based Hallucination Judge arXiv:2407.12943**Binjie Wang, Steffi Chern, Ethan Chern, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-halu-j-critique-based-hallucination/)
46. **[Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval](https://md.research.elamur.ai/free/en/papers/sharma-2026-reducing-hallucinations-language-model-based/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Aditya Sharma, Christopher Pal, Amal Zouaq*2026 · 5 citations*
47. **[SciClaims: An End-to-End Generative System for Biomedical Claim Analysis](https://md.research.elamur.ai/free/en/papers/ortega-2025-sciclaims-end-end-generative-system/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Raul Ortega, Jose Manuel Gomez-Perez*2025 · 4 citations*
48. [**Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models arXiv:2412.15265**Yingshui Tan, Boren Zheng, Baihui Zheng, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/tan-2024-chinese-safetyqa-safety-short/)
49. [**HalluTruthQA: A Fine-Grained Benchmark for Hallucination Detection, Localization, and Explanation in Arabic Question Answering arXiv:2607.20219**Abdessalam 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 Hadid*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/bouchekif-2026-hallutruthqa-fine-grained-benchmark-hallucination/)
50. **[HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy](https://md.research.elamur.ai/free/en/papers/xu-2026-had-hallucination-detection-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fan Xu, Xinyu Hu, Zhenghan Yu, et al.*2026 · 1 citation*
51. [**HalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth Verification arXiv:2608.03966**Salah Eddine Bekhouche; Abdessalam Bouchekif; Hichem Telli; Mohammed-En-Nadhir Zighem;, Abdenour Hadid*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/bekhouche-2026-hallutruthqa-4k-fine-grained-corpus/)
52. **[Assessing Web Search Credibility and Response Groundedness in Chat Assistants](https://md.research.elamur.ai/free/en/papers/vykopal-2026-assessing-web-search-credibility-response/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Ivan Vykopal, Matúš Pikuliak, Simon Ostermann, et al.*2026*
53. **[Detecting Hallucinations in Authentic LLM–Human Interactions](https://md.research.elamur.ai/free/en/papers/ren-2026-detecting-hallucinations-authentic-llm-human/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yujie Ren, Niklas Gruhlke, Anne Lauscher*2026*
54. **[Distill and Align Decomposition for Enhanced Claim Verification](https://md.research.elamur.ai/free/en/papers/magomere-2026-distill-align-decomposition-enhanced-claim/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jabez Magomere, Elena Kochkina, Samuel Mensah, et al.*2026*
55. **[FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs](https://md.research.elamur.ai/free/en/papers/sawczyn-2026-factselfcheck-fact-level-black-box/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Albert Sawczyn, Jakub Binkowski, Denis Janiak, et al.*2026*
56. **[From Conversation to Evaluation: Benchmarking LLMs on Development Knowledge via SimpleDevQA](https://md.research.elamur.ai/free/en/papers/zhang-2026-conversation-evaluation-benchmarking-llms-development/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Zhang, Lianghong Guo, Yanlin Wang, et al.*2026*
57. **[JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation](https://md.research.elamur.ai/free/en/papers/xu-2026-jointcq-improving-factual-hallucination-detection/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fan Xu, Huixuan Zhang, Zhenliang Zhang, et al.*2026*
58. **[Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments](https://md.research.elamur.ai/free/en/papers/mi-2026-logical-consistency-as-bridge-improving/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Mi, Qiang Sheng, Shaofei Wang, et al.*2026*
59. **[PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations](https://md.research.elamur.ai/free/en/papers/wu-2026-prism-probing-reasoning-instruction-source/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuhe Wu, Guangyu Wang, Yuran Chen, et al.*2026*
60. **[ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations](https://md.research.elamur.ai/free/en/papers/wang-2026-refact-benchmark-scientific-confabulation-detection/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yindong Wang, Martin Preiß, Margarita Bugueño, et al.*2026*
61. **[Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024*

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