mlsanityData to 14 Oct 2026

Sycophancy in Large Language Models: Causes and Mitigations

arXiv 2411.15287 ↗First posted 22 Nov 2024cs.CL cs.AIcited by 101

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Abstract (arXiv)

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to exhibit sycophantic behavior - excessively agreeing with or flattering users - poses significant risks to their reliability and ethical deployment. This paper provides a technical survey of sycophancy in LLMs, analyzing its causes, impacts, and potential mitigation strategies. We review recent work on measuring and quantifying sycophantic tendencies, examine the relationship between sycophancy and other challenges like hallucination and bias, and evaluate promising techniques for reducing sycophancy while maintaining model performance. Key approaches explored include improved training data, novel fine-tuning methods, post-deployment control mechanisms, and decoding strategies. We also discuss the broader implications of sycophancy for AI alignment and propose directions for future research. Our analysis suggests that mitigating sycophancy is crucial for developing more robust, reliable, and ethically-aligned language models.

Key facts. Cited by 101 papers in the map (data through 14 Oct 2026). Builds on 16 of its 19 references that are in the map. First posted on arXiv 22 Nov 2024.

Builds on

16 of 19 references.

References · 16

  1. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  2. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  3. 2023Simple synthetic data reduces sycophancy in large language modelscited by 187
  4. 2024Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Modelscited by 178
  5. 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 127
  6. 2024Language Models Learn to Mislead Humans via RLHFcited by 124
  7. 2023System 2 Attention (is something you might need too)cited by 89
  8. 2023Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experimentcited by 77
  9. 2024Steering Without Side Effects: Improving Post-Deployment Control of Language Modelscited by 46
  10. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
  11. 2024Sycophancy in Vision-Language Models: A Systematic Analysis and an Inference-Time Mitigation Frameworkcited by 17
  12. 2024ControlLM: Crafting Diverse Personalities for Language Modelscited by 15
4 more
  1. 2024Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategiescited by 10
  2. 2024Understanding Knowledge Drift in LLMs through Misinformationcited by 10
  3. 2024AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directionscited by 8
  4. 2024It Takes Two: On the Seamlessness between Reward and Policy Model in RLHFcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 39; 2026: 62.

101 papers.

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  1. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 205
  2. 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 129
  3. 2025SycEval: Evaluating LLM Sycophancycited by 108
  4. 2025Measuring Sycophancy of Language Models in Multi-turn Dialoguescited by 104
  5. 2025LLM Social Simulations Are a Promising Research Methodcited by 99
  6. 2025When Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Modelscited by 50
  7. 2025The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMscited by 36
  8. 2025TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Modelscited by 33
  9. 2025BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMscited by 27
  10. 2026OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agentscited by 22
  11. 2025The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Processcited by 21
  12. 2025Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMscited by 21
38 more
  1. 2025WebGuard: Building a Generalizable Guardrail for Web Agentscited by 21
  2. 2026Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesianscited by 21
  3. 2025Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QAcited by 18
  4. 2026What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Constructcited by 16
  5. 2025Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositionscited by 15
  6. 2025Potemkin Understanding in Large Language Modelscited by 15
  7. 2025Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PDcited by 13
  8. 2026MemSyco-Bench: Benchmarking Sycophancy in Agent Memorycited by 12
  9. 2025From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agentscited by 10
  10. 2025EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Modelscited by 9
  11. 2025Enhancing Large Language Model Reasoning with Reward Models: An Analytical Surveycited by 8
  12. 2026SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancycited by 8
  13. 2025"Check My Work?": Measuring Sycophancy in a Simulated Educational Contextcited by 7
  14. 2025Sycophantic AI Decreases Prosocial Intentions and Promotes Dependencecited by 6
  15. 2026Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systemscited by 6
  16. 2026The Impact of AI-Generated Text on the Internetcited by 6
  17. 2026When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressurecited by 6
  18. 2025Mechanistic Anomaly Detection for "Quirky" Language Modelscited by 5
  19. 2025Investigating VLM Hallucination from a Cognitive Psychology Perspective: A First Step Toward Interpretation with Intriguing Observationscited by 4
  20. 2025AI Debaters are More Persuasive when Arguing in Alignment with Their Own Beliefscited by 4
  21. 2025Knowing What You Know Is Not Enough: Large Language Model Confidences Don't Align With Their Actionscited by 4
  22. 2025PENDULUM: A Benchmark for Assessing Sycophancy in Multimodal Large Language Modelscited by 4
  23. 2026Courtroom-Style Multi-Agent Debate with Progressive RAG and Role-Switching for Controversial Claim Verificationcited by 4
  24. 2026Pressure, What Pressure? Sycophancy Disentanglement in Language Models via Reward Decompositioncited by 4
  25. 2026Decomposing Factual Sycophancy in Language Models: How Size and Instruction Tuning Shape Robustnesscited by 4
  26. 2025Cognitive models can reveal interpretable value trade-offs in language modelscited by 3
  27. 2025Pun Unintended: LLMs and the Illusion of Humor Understandingcited by 3
  28. 2025When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Beingcited by 3
  29. 2026The Synthetic Web: Adversarially-Curated Mini-Internets for Diagnosing Epistemic Weaknesses of Language Agentscited by 3
  30. 2026One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Modelscited by 3
  31. 2026Invisible failures in human-AI interactionscited by 3
  32. 2025Towards Operational Validation of LLM-Agent Social Simulations: A Replicated Study of a Reddit-like Technology Forumcited by 2
  33. 2025Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanationscited by 2
  34. 2025Social Simulations with Large Language Model Risk Utopian Illusioncited by 2
  35. 2026Acting Flatterers via LLMs Sycophancy: Combating Clickbait with LLMs Opposing-Stance Reasoningcited by 2
  36. 2026A Few Bad Neurons: Isolating and Surgically Correcting Sycophancycited by 2
  37. 2026When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Makingcited by 2
  38. 2026This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QAcited by 2

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  1. 2026Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models
  2. 2024Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs
  3. 2025Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models
  4. 2026SPINE: Measuring LLM Sycophancy under Sustained Multi-Turn Pressure
  5. 2024Linear Probe Penalties Reduce LLM Sycophancy
  6. 2026Dissociating the Internal Representations of Sycophancy in LLMs
  7. 2023Large Language Model Alignment: A Survey
  8. 2025Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy