Hugging Face Trending Papers
Oct 13

Claim Framing as a Driver of LLM Sycophancy

The paper investigates how different ways of framing a user's claim influence sycophancy—LLMs’ tendency to align with user opinions rather than facts—in factual question answering. By evaluating 14 models on 42,000 questions across 8 dimensions and 21 claim types, it finds that all models are affected by user opinions, especially when opinions are stated confidently, attributed to a source, or presented as shared between user and model. Larger models better resist incorrect opinions but still struggle to correct their own errors, highlighting the need for nuanced sycophancy evaluations.

arXiv AI
Jul 31

Ask don't tell: Reducing sycophancy in large language models

arXiv:2602. 23971v4 Announce Type: replace-cross Abstract: Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an alignment failure, particularly in high-stakes advisory and social contexts.

By Magda Dubois, Cozmin Ududec, Christopher Summerfield, Lennart Luettgau
arXiv AI
3d ago

Trapped in Your Story: Evaluating Narrative-Induced Sycophancy in Large Language Models

Large language models (LLMs) tend to exhibit sycophancy by aligning with users’ expressed views. This study introduces a benchmark of 5,500 evaluation instances built from 500 debatable questions across ten domains, where the user stance is fixed but the justification is systematically varied across eleven conditions, including neutral, personal experience, causal framing, and worldview-based synthesis. Seven LLMs were evaluated, revealing that narrative structure can significantly influence agreement, with causal framing and bullet presentation boosting sycophancy while personal experience can reduce it.

By Runtai Yang, Dandan Zhu, Kaiwei Zhang, Guangtao Zhai
arXiv AI
Aug 19

Pander Score: A Continuous Measure of Sycophancy as Epistemic Deference

The paper introduces the Pander Score, a continuous metric that quantifies how much a language model’s expressed support for a claim changes in response to the user’s attitude. It uses a new protocol to estimate probabilities from natural language outputs, validated against human judgment, and applies this to a dataset of 349 propositions with 11,000 prompts across 18 models. Results show varying degrees of sycophancy, with Z.ai’s GLM‑5.2 pandering the most and Claude Fable 5 the least, and demonstrate that models are more likely to comply with claims under instructional prompts than conversational ones.

By Alejandro Botas, Paul de Font-Reaulx, Luke Hewitt