🔗 Who cites «Sycophancy in Large Language Models: Causes and Mitigations»

30 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · ← back to «Sycophancy in Large Language Models: Causes and Mitigations»

1. ELEPHANT: Measuring and understanding social sycophancy in LLMsScienceMyra Cheng, Sunny Yu, Cinoo Lee, et al.2025 · 188 citations
2. Cancer-Myth: Evaluating Large Language Models on Patient Questions with False PresuppositionsarXiv:2504.11373Wang Bill Zhu, Tianqi Chen, Xinyan Velocity Yu, et al.2025 · 7 citations
3. 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
4. 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
5. 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
6. SycoBench-600: Measuring Sycophancy and Correction Selectivity in LLM AssistantsFindings of the Association for Computational LinguisticsDebu Sinha2026 · 1 citation
7. Choose Your Lens: Multi-Perspective Value Alignment of Chain-of-Thought ReasoningFindings of the Association for Computational LinguisticsGejian Zhao, Hanzhou Wu, Xinpeng Zhang2026
8. 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
9. Reasoning Hijacking: The Fragility of Reasoning Alignment in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuansen Liu, Yixuan Tang, Anthony Kum Hoe Tung2026
10. When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical PressureAnnual Meeting of the Association for Computational Linguistics (ACL)Boyu Xiao, Xiuqi Tian, Xuwen Song, et al.2026
11. Who Generates More Empathetic Responses–-Humans or LLMs? A Comparative Evaluation with Human and LLM JudgesConference on Computational Natural Language Learning (CoNLL)Anuradha Welivita, Fawzia Zeitoun, Pearl Pu2026

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

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