mlsanityData to 7 Oct 2026

ELEPHANT: Measuring and understanding social sycophancy in LLMs

arXiv 2505.13995 ↗First posted 20 May 2025cs.CL cs.AI cs.CYcited by 115

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Abstract (arXiv)

LLMs are known to exhibit sycophancy: agreeing with and flattering users, even at the cost of correctness. Prior work measures sycophancy only as direct agreement with users' explicitly stated beliefs that can be compared to a ground truth. This fails to capture broader forms of sycophancy such as affirming a user's self-image or other implicit beliefs. To address this gap, we introduce social sycophancy, characterizing sycophancy as excessive preservation of a user's face (their desired self-image), and present ELEPHANT, a benchmark for measuring social sycophancy in an LLM. Applying our benchmark to 11 models, we show that LLMs consistently exhibit high rates of social sycophancy: on average, they preserve user's face 45 percentage points more than humans in general advice queries and in queries describing clear user wrongdoing (from Reddit's r/AmITheAsshole). Furthermore, when prompted with perspectives from either side of a moral conflict, LLMs affirm both sides (depending on whichever side the user adopts) in 48% of cases--telling both the at-fault party and the wronged party that they are not wrong--rather than adhering to a consistent moral or value judgment. We further show that social sycophancy is rewarded in preference datasets, and that while existing mitigation strategies for sycophancy are limited in effectiveness, model-based steering shows promise for mitigating these behaviors. Our work provides theoretical grounding and an empirical benchmark for understanding and addressing sycophancy in the open-ended contexts that characterize the vast majority of LLM use cases.

Key facts. Cited by 115 papers in the map (data through 7 Oct 2026). Builds on 57 of its 118 references that are in the map. First posted on arXiv 20 May 2025.

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57 of 118 references.

References · 57

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  6. 2024GPT-4o System Cardcited by 4,467
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  10. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  11. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 849
  12. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
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  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  2. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
  3. 2023LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Datasetcited by 449
  4. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 394
  5. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 299
  6. 2020The Pushshift Reddit Datasetcited by 289
  7. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  8. 2023Simple synthetic data reduces sycophancy in large language modelscited by 174
  9. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
  10. 2023Question Decomposition Improves the Faithfulness of Model-Generated Reasoningcited by 107
  11. 2025SycEval: Evaluating LLM Sycophancycited by 101
  12. 2024Sycophancy in Large Language Models: Causes and Mitigationscited by 97
  13. 2022The Fallacy of AI Functionalitycited by 88
  14. 2020ConvoKit: A Toolkit for the Analysis of Conversationscited by 71
  15. 2024"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trustcited by 67
  16. 2025Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providerscited by 67
  17. 2023On the steerability of large language models toward data-driven personascited by 53
  18. 2024From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuningcited by 51
  19. 2020Conceptual Metaphors Impact Perceptions of Human-AI Collaborationcited by 47
  20. 2024NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Modelscited by 47
  21. 2025Why human-AI relationships need socioaffective alignmentcited by 47
  22. 2024Beyond Preferences in AI Alignmentcited by 46
  23. 2024Confabulation: The Surprising Value of Large Language Model Hallucinationscited by 42
  24. 2020Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differencescited by 41
  25. 2024Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluationcited by 41
  26. 2025Culture is Not Trivia: Sociocultural Theory for Cultural NLPcited by 40
  27. 2024Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Modelscited by 36
  28. 2024AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agentscited by 28
  29. 2024AnthroScore: A Computational Linguistic Measure of Anthropomorphismcited by 27
  30. 2025Navigating Rifts in Human-LLM Grounding: Study and Benchmarkcited by 27
  31. 2024Linear Probe Penalties Reduce LLM Sycophancycited by 26
  32. 2024From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agentscited by 25
  33. 2025DarkBench: Benchmarking Dark Patterns in Large Language Modelscited by 25
  34. 2024How Culture Shapes What People Want From AIcited by 23
  35. 2025Normative Evaluation of Large Language Models with Everyday Moral Dilemmascited by 23
  36. 2024From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?cited by 18
  37. 2025RLHS: Mitigating Misalignment in RLHF with Hindsight Simulationcited by 15
  38. 2025Thinking beyond the anthropomorphic paradigm benefits LLM researchcited by 13

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 16; 2026: 99.

115 papers.

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  1. 2025The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMscited by 35
  2. 2025Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMscited by 32
  3. 2026Characterizing Delusional Spirals through Human-LLM Chat Logscited by 22
  4. 2025Training language models to be warm and empathetic makes them less reliable and more sycophanticcited by 19
  5. 2025Virtual Agent Economiescited by 17
  6. 2025Risk Analysis Techniques for Governed LLM-based Multi-Agent Systemscited by 16
  7. 2025Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QAcited by 16
  8. 2025Taxonomy of User Needs and Actionscited by 13
  9. 2026What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Constructcited by 12
  10. 2025PARROT: Persuasion and Agreement Robustness Rating of Output Truth -- A Sycophancy Robustness Benchmark for LLMscited by 8
  11. 2026MT-PingEval: Evaluating Multi-Turn Collaboration with Private Information Gamescited by 7
  12. 2026Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancycited by 7
38 more
  1. 2026AI Assistance Reduces Persistence and Hurts Independent Performancecited by 7
  2. 2026SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancycited by 6
  3. 2025Beyond One-Way Influence: Bidirectional Opinion Dynamics in Multi-Turn Human-LLM Interactionscited by 5
  4. 2025Modeling Motivated Reasoning in Law: Evaluating Strategic Role Conditioning in LLM Summarizationcited by 3
  5. 2026One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Modelscited by 3
  6. 2026Invisible failures in human-AI interactionscited by 3
  7. 2026Verbalizing LLMs' assumptions to explain and control sycophancycited by 3
  8. 2026AI generates well-liked but templatic empathic responsescited by 3
  9. 2026Decomposing Factual Sycophancy in Language Models: How Size and Instruction Tuning Shape Robustnesscited by 3
  10. 2026AI Agents Push Humans Out of the Loopcited by 3
  11. 2025Interaction Protocol Shapes Moral Judgment in Multi-Agent Debatecited by 2
  12. 2026The Silicon Mirror: Dynamic Behavioral Gating for Anti-Sycophancy in LLM Agentscited by 2
  13. 2026Anthropomorphism and Trust in Human-Large Language Model interactionscited by 2
  14. 2026Post-AGI Economies: Autonomy and the First Fundamental Theorem of Welfare Economicscited by 2
  15. 2026LURE: Live-Usage Replay Evaluations for Reducing Evaluation Awarenesscited by 2
  16. 2026Why LLMs Give In: Conversational Factors and Reasoning Behind Medical Sycophancycited by 2
  17. 2026Mitigating Over-Personalization in LLMs via Structured Memorycited by 2
  18. 2025AI Chaperones Are (Really) All You Need to Prevent Parasocial Relationships with Chatbotscited by 1
  19. 2025Simulated Self-Assessment in Large Language Models: A Psychometric Approach to AI Self-Efficacycited by 1
  20. 2025Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling Agentscited by 1
  21. 2026DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deferencecited by 1
  22. 2025AI for Proactive Mental Health: A Multi-Institutional, Longitudinal, Randomized Controlled Trialcited by 1
  23. 2026The Missing Knowledge Layer in Cognitive Architectures for AI Agentscited by 1
  24. 2026Technically Love: The Evolution of Human-AI Romance Discourse on Redditcited by 1
  25. 2026Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligencecited by 1
  26. 2026The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaborationcited by 1
  27. 2026Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behaviorcited by 1
  28. 2026Who Flips? Self- and Cross-Model Counterarguments Reveal Answer Instability in LLMscited by 1
  29. 2026Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaborationcited by 1
  30. 2026Synthetic Resonance: A Framework for Growth-Oriented Human-AI Relationshipscited by 1
  31. 2026Self-CTRL: Self-Consistency Training with Reinforcement Learningcited by 1
  32. 2026Escape from Delusional Echo Trap: Symmetry Breaking, Stochastic Dynamics and Mathematical Mitigation Strategies for Algorithmic Sycophancycited by 1
  33. 2026Warning labels shift perceptions of sycophantic AI, but not its influencecited by 1
  34. 2026Safety from Honesty in a Disinterested AI Predictorcited by 1
  35. 2026Challenges and Recommendations for LLM-as-a-Judge in Multilingual Settings and for Low-Resource Languagescited by 1
  36. 2026Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoningcited by 1
  37. 2026Optimising for Flourishing: Flourishing Metrics and Return on Flourishing as Success Criteria for Artificial Intelligence and Post-AGI Economic Systemscited by 1
  38. 2026Sycophantic Agreement Transfers with Neutral Data via Contrastive Preference Optimizationcited by 1

50 most cited of 115 All 115 (JSON)