🔗 Who cites «Simple synthetic data reduces sycophancy in large language models»

107 papers of our library cite it (of the 149,439 whose bibliographies we read) · 40 with a page here, the most cited first · ← back to «Simple synthetic data reduces sycophancy in large language models»

1. 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
2. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
3. 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
4. Explainability for Large Language Models: A SurveyACM Transactions on Intelligent Systems and TechnologyHaiyan Zhao, Hanjie Chen, Fan Yang, et al.2023 · 790 citations
5. 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
6. Towards Understanding Sycophancy in Language ModelsarXiv:2310.13548Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.2023 · 384 citations
7. MAmmoTH: Building Math Generalist Models through Hybrid Instruction TuningarXiv:2309.05653Xiang Yue, Xingwei Qu, Ge Zhang, et al.2023 · 295 citations
8. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
9. The Prompt Report: A Systematic Survey of Prompt Engineering TechniquesarXiv:2406.06608Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.2024 · 189 citations
10. ELEPHANT: Measuring and understanding social sycophancy in LLMsScienceMyra Cheng, Sunny Yu, Cinoo Lee, et al.2025 · 188 citations
11. 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
12. Sycophancy in Large Language Models: Causes and MitigationsLecture notes in networks and systemsLars Malmqvist2024 · 123 citations
13. Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language ModelsarXiv:2501.09686Fengli Xu, Qianyue Hao, Zefang Zong, et al.2025 · 120 citations
14. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
15. GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem SolversAnnual Meeting of the Association for Computational Linguistics (ACL)Qintong Li, Leyang Cui, Xueliang Zhao, et al.2024 · 108 citations
16. 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
17. Alignment for HonestyConference on Neural Information Processing Systems (NeurIPS)Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.2023 · 90 citations
18. MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to UsearXiv:2310.03128Yue Huang, Jiawen Shi, Yuan Li, et al.2023 · 81 citations
19. 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
20. Language Models in Dialogue: Conversational Maxims for Human-AI InteractionsFindings of the Association for Computational LinguisticsErik Miehling, Manish Nagireddy, Prasanna Sattigeri, et al.2024 · 23 citations
21. DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphConference on Neural Information Processing Systems (NeurIPS)Zhehao Zhang, Jiaao Chen, Diyi Yang2024 · 17 citations
22. Evaluating Large Language Models for Health-related Queries with PresuppositionsFindings of the Association for Computational LinguisticsNavreet Kaur, Monojit Choudhury, Danish Pruthi2024 · 15 citations
23. Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal ContextarXiv:2412.17596Kai Ruan, Xuan Wang, Jixiang Hong, et al.2024 · 13 citations
24. Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Wenrui Zhou, Mohamed Hendy, Shu Yang, et al.2026 · 6 citations
25. Task Matters: Knowledge Requirements Shape LLM Responses to Context–Memory ConflictFindings of the Association for Computational LinguisticsKaiser Sun, Fan Bai, Mark Dredze2026 · 3 citations
26. From Fact to Judgment: Investigating the Impact of Task Framing on LLM Conviction in Dialogue SystemsInternational Workshop on Spoken Dialogue System TechnologyParisa Rabbani, Nimet Beyza Bozdag, Dilek Hakkani-Tur2026 · 2 citations
27. Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?Annual Meeting of the Association for Computational Linguistics (ACL)Lorenzo Molfetta, Alessio Cocchieri, Luca Ragazzi, et al.2026 · 2 citations
28. Sounding vs. Being an Expert: Disentangling Authority, Register and Cultural Impact in Sycophantic LLMsFindings of the Association for Computational LinguisticsGabriele Maraia, Fabio Massimo Zanzotto, Leonardo Ranaldi2026 · 1 citation
29. SycoBench-600: Measuring Sycophancy and Correction Selectivity in LLM AssistantsFindings of the Association for Computational LinguisticsDebu Sinha2026 · 1 citation
30. Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language ModelsWorkshop on Generation, Evaluation and Metrics (GEM)Priyanka Mary Mammen, Emil Joswin, Shankar Venkitachalam2026 · 1 citation
31. Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling AgentsConference of the European Chapter of the Association for Computational Linguistics (EACL)Daud Waqas, Aaryamaan Golthi, Erika Hayashida, et al.2026
32. DialDefer: A Framework for Detecting and Mitigating LLM Dialogic DeferenceAnnual Meeting of the Association for Computational Linguistics (ACL)Parisa Rabbani, Priyam Sahoo, Ruben Mathew, et al.2026
33. Evaluating Language Model Pluralism through In-the-wild Crowd DiscussionsAnnual Meeting of the Association for Computational Linguistics (ACL)Gagan Mundada, Rohan Surana, Nandhini Swaminathan, et al.2026
34. Feeling Right vs. Being Right: How AI Sycophancy Affects Value-Laden DeliberationAnnual Meeting of the Association for Computational Linguistics (ACL)Jeongwoo Ryu, Soomin Kim, Jinsu Eun, et al.2026
35. I Understand How You Feel: Enhancing Deeper Emotional Support Through Multilingual Emotional Validation in Dialogue SystemAnnual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)Zi Haur Pang, Yahui Fu, Koji Inoue, et al.2026
36. Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood ConsistencyAnnual Meeting of the Association for Computational Linguistics (ACL)Haoming Xu, Ningyuan Zhao, Yunzhi Yao, et al.2026
37. Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-AnsweringFindings of the Association for Computational LinguisticsAtharv Naphade2026
38. Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Arya Shah, Deepali Mishra, Chaklam Silpasuwanchai2026
39. User-Assistant Bias in LLMsFindings of the Association for Computational LinguisticsXu Pan, Jingxuan Fan, Zidi Xiong, et al.2026
40. When Facts Change: Temporal Knowledge Conflict Resolution in LLMsFindings of the Association for Computational LinguisticsJonas Wallat, Wolfgang Nejdl, Sandipan Sikdar2026

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