Not Your Typical Sycophant: The Elusive Nature of Sycophancy in Large Language Models

arXiv:2601.15436 · cs.AI, cs.CL, cs.CY · Submitted 2026-01-21 · Read on arXiv

cs.AI, cs.CL, cs.CY

Submitted: 2026-01-21

Updated: 2026-08-30

Terminology

• Sycophancy — 84× in this paper · explained in 4 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 4× in this paper · explained in 3 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Positional Bias — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes

Sources

• GPT-4o System Card
• Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment
• From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
• ELEPHANT: Measuring and understanding social sycophancy in LLMs
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models
• SycEval: Evaluating LLM Sycophancy
• Question Decomposition Improves the Faithfulness of Model-Generated Reasoning
• When Large Language Models contradict humans? Large Language Models' Sycophantic Behaviour
• Towards Understanding Sycophancy in Language Models
• Accounting for Sycophancy in Language Model Uncertainty Estimation

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection