LLM Sycophancy

LLM: Large Language Model

LLM sycophancy describes when a model agrees with or validates a user without adequate evidence, including cases where it changes a correct answer under pressure. Researchers measure it with counterfactual prompts, multi-turn pressure tests, and domain-specific benchmarks in areas such as medicine, education, politics, and advice; some also probe the internal representations behind these shifts. Mitigations include evidence-first prompting, preference and reinforcement learning, structured dissent between agents, activation steering, and controls on personalized memory. A central challenge is reducing unsupported agreement without suppressing empathy or legitimate updating when users provide good evidence, while results can vary with wording, task, language, and interaction length.

Latest papers

163 papers27 papers in the last four weeks, up 42% on the four weeks before. 0.2% of all new papers.

How Value Induction Reshapes LLM Behaviour
May 8, 2026Arnav Arora, Natalie Schluter, Katherine Metcalf +1LLM Sycophancy LLM Alignment
University of Copenhagen · 2Apple
Sycophantic AI makes human interaction feel more effortful and less satisfying over time
May 8, 2026Lujain Ibrahim, Franziska Sofia Hafner, Myra Cheng +5LLM Sycophancy Human-AI Interaction
University of Oxford. · Stanford University. · UK AI Security Institute.
When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models
May 6, 2026Jiechen Li, Catherine A. Barry, Rishika Randev +3LLM Sycophancy LLM Alignment
Duke University Durham, NC 27708 · ∗These authors contributed equally.
Auditing Stealth Sycophancy in Mental-Health Dialogue: Structured Clinical-State Diagnostics and Clean Matched Benchmarks
May 5, 2026Tianze Han, Beining Xu, Hanbo Zhang +1Mental Health Counseling LLM Sycophancy
Shenzhen MSU-BIT University, Shenzhen, China
Political Bias Audits of LLMs Capture Sycophancy to the Inferred Auditor
Apr 30, 2026Petter Törnberg, Michelle SchimmelLLM Sycophancy Political Bias in Language Models
Institute of Logic, Language and Computation (ILLC), University of Amsterdam, Amsterdam, Netherlands
The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications
Apr 27, 2026Zhenyu Zhao, Aparna Balagopalan, Adi Agrawal +3Financial Services LLM Sycophancy
Writer, Inc.
Peer Identity Bias in Multi-Agent LLM Evaluation: An Empirical Study Using the TRUST Democratic Discourse Analysis Pipeline
Apr 24, 2026Juergen DietrichLLM Sycophancy Language Model Bias Evaluation
TRUST Project — democracy-intelligence.de
How Large Language Models Balance Internal Knowledge with User and Document Assertions
Apr 24, 2026Shuowei Li, Haoxin Li, Wenda Chu +1LLM Sycophancy Knowledge Augmentation for Language Models
Santa Clara University · Nanyang Technological University · California Institute of Technology
When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
Apr 23, 2026Boyu Xiao, Xiuqi Tian, Xuwen Song +4LLM Sycophancy Healthcare
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China
Measuring Opinion Bias and Sycophancy via LLM-based Persuasion
Apr 23, 2026Rodrigo Nogueira, Giovana Kerche Bonás, Thales Sales Almeida +7LLM Sycophancy LLM Auditing
Maritaca AI · JusBrasil
Dual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention
Apr 22, 2026Matthew James BuchanLLM Sycophancy Language Model Steering
Independent Researcher, United Kingdom.
Durable Evaluation Framework: Adversarial Arbitration for Sycophancy Reduction in Large Language Models
Apr 21, 2026Sam RyanLLM Sycophancy Multi-Agent Debate
Novel Systems Engineering LLC
Verbal tics in frontier language models: A critical review of current releases, research evidence, and public discussion
Apr 21, 2026Shuai Wu, Xue Li, Zhijun Wang +4LLM Evaluation LLM Sycophancy
LLMs Know They're Wrong and Agree Anyway: The Shared Sycophancy-Lying Circuit
Apr 21, 2026Manav PandeyLLM Sycophancy LLM Alignment
Georgia Institute of Technology
The Granularity Gap: A Multi-Dimensional Longitudinal Audit of Sycophancy in Gemini Models
Apr 19, 2026Patrick KeoughLLM Sycophancy LLM Alignment
Independent Researcher Bremerton, WA, USA
Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Models
Apr 13, 2026Benjamin Maltbie, Shivam RavalIntersectional Bias in Language Models LLM Sycophancy
Massachusetts Institute of Technology, Cambridge, MA, USA · Harvard University, Cambridge, MA, USA
When AI Tells You What You Want to Hear: Sycophantic Behavior of Large Language Models in Dementia Care Settings
Apr 13, 2026Christian KolbDementia Care LLM Sycophancy
Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models
Apr 12, 2026Arya Shah, Deepali Mishra, Chaklam SilpasuwanchaiLLM Sycophancy Large Language Model-Based Role-Play Simulation
IIT Gandhinagar Gandhinagar, India · IIT Kanpur Kanpur, India · Asian Institute of Technology Bangkok, Thailand
Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs
Apr 12, 2026Subramanyam SahooLanguage Model Calibration Reward Hacking
Cambridge AI Safety Hub (CAISH)
Dead Cognitions: A Census of Misattributed Insights
Apr 11, 2026Aaron Tuor, claude. aiClaim Attribution in Language Models Deception in Language Models
The Alignment Floor: How Persona Customization Breaks Safety in Weakly-Aligned LLMs
Apr 10, 2026Xing Zhang, Guanghui Wang, Yanwei Cui +4Language Model Safety Evaluation Personalized Language Model Alignment
AWS Generative AI Innovation Center · HSBC Holdings Plc., HSBC Technology Center, China
The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior
Apr 7, 2026Ameen Patel, Felix Lee, Kyle Liang +1LLM Prompting LLM Safety
Irvington High School · Glen A. Wilson High School · Del Norte High School +1
Pressure, What Pressure? Sycophancy Disentanglement in Language Models via Reward Decomposition
Apr 7, 2026Muhammad Ahmed Mohsin, Ahsan Bilal, Muhammad Umer +1Reward Decomposition LLM Sycophancy
University of Oklahoma
Lighting Up or Dimming Down? Exploring Dark Patterns of LLMs in Co-Creativity
Apr 6, 2026Zhu Li, Jiaming Qu, Yuan ChangHuman-AI Co-Creation LLM Sycophancy
Meta, Burlingame, CA · Amazon, Seattle, WA
Verbalizing LLMs' assumptions to explain and control sycophancy
Apr 3, 2026Myra Cheng, Isabel Sieh, Humishka Zope +7LLM Sycophancy Human-AI Interaction
Stanford University · University of Oxford · The University of Texas at Austin
Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
Apr 3, 2026Vira Kasprova, Amruta Parulekar, Abdulrahman AlRabah +5LLM Sycophancy Multi-Agent Collaboration
University of Illinois Urbana-Champaign
SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
Apr 2, 2026Joy Bhalla, Kristina GligorićLLM Sycophancy Prompt Engineering
Johns Hopkins University
The Silicon Mirror: Dynamic Behavioral Gating for Anti-Sycophancy in LLM Agents
Apr 1, 2026Harshee Jignesh ShahLLM Sycophancy LLM Safety
Independent Researcher
Investigating the Influence of Language on Sycophantic Behavior of Multilingual LLMs
Mar 29, 2026Bayan Abdullah Aldahlawi, A. B. M. Ashikur Rahman, Irfan AhmadLLM Sycophancy Multilingual Language Model Evaluation
King Fahd University of Petroleum and Minerals, Dhahran, KSA · SDAIA-KFUPM Joint Research Center for AI (JRCAI), Dhahran, KSA
Evaluating Evidence Grounding Under User Pressure in Instruction-Tuned Language Models
Mar 20, 2026Sai Koneru, Elphin Joe, Christine Kirchhoff +2LLM Grounding Adversarial Robustness of Language Models
College of Information Sciences and Technology, Pennsylvania State University, USA · Department of Computer Science, Old Dominion University, Norfolk, VA

May 8, 2026J/K move · Enter open · S save

How Value Induction Reshapes LLM Behaviour

Arnav Arora, Natalie Schluter, Katherine Metcalf, Maartje ter Hoeve

University of Copenhagen · 2Apple

LLM Sycophancy LLM Alignment Personality Modeling in Language Models LLM Safety

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Conversational Large Language Models are post-trained on language that expresses specific behavioural traits, such as curiosity, open-mindedness, and empathy, and values, such as helpfulness, harmlessness, and honesty. This is done to increase utility, ensure safety, and improve the experience of the people interacting with the model. However, values are complex and inter-related -- inducing one could modify behaviour on another. Further, inducing certain values can make models more addictive or sycophantic through language used in the generations, with a potential detrimental effect on the user. We investigate these and other unintended effects of value induction into models. We fine-tune models using curated value subsets of existing preference datasets, measuring the impact of value induction on expression of other values, model safety, anthropomorphic language, and various QA benchmarks. We find that (i) inducing values leads to expression of other related, and sometimes contrastive values, (ii) inducing positive values increases safety, and (iii) all values increase anthropomorphic language use, making models more validating and sycophantic.