mlsanityData to 7 Oct 2026

Sycophancy Claims about Language Models: The Missing Human-in-the-Loop

arXiv 2512.00656 ↗First posted 29 Nov 2025cs.CL cs.CYcited by 4

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Abstract (arXiv)

Sycophantic response patterns in Large Language Models (LLMs) have been increasingly claimed in the literature. We review methodological challenges in measuring LLM sycophancy and identify five core operationalizations. Despite sycophancy being inherently human-centric, current research does not evaluate human perception. Our analysis highlights the difficulties in distinguishing sycophantic responses from related concepts in AI alignment and offers actionable recommendations for future research.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 29 Nov 2025.

Builds on

16 of 23 references.

References · 16

  1. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  2. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
  3. 2018Gender Bias in Coreference Resolutioncited by 438
  4. 2024Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Modelscited by 158
  5. 2024From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuningcited by 51
  6. 2024NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Modelscited by 47
  7. 2024Monitoring Latent World States in Language Models with Propositional Probescited by 39
  8. 2023How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilitiescited by 33
  9. 2024Prompt Leakage effect and defense strategies for multi-turn LLM interactionscited by 23
  10. 2025Understanding Impact of Human Feedback via Influence Functionscited by 11
  11. 2024Refined Direct Preference Optimization with Synthetic Data for Behavioral Alignment of LLMscited by 10
  12. 2024Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategiescited by 10
4 more
  1. 2024Multi-objective Reinforcement learning from AI Feedbackcited by 9
  2. 2024Measuring Agreeableness Bias in Multimodal Modelscited by 4
  3. 2024Unmasking the Shadows of AI: Investigating Deceptive Capabilities in Large Language Modelscited by 2
  4. 2024Social Evolution of Published Text and The Emergence of Artificial Intelligence Through Large Language Models and The Problem of Toxicity and Biascited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Moral Sycophancy in Vision Language Modelscited by 3
  2. 2026The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Modelscited by 0
  3. 2026Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarkscited by 0
  4. 2026Are You Sure You're Sure? Two Confounds in a Sycophancy Benchmarkcited by 0