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 Computation and Language
Aug 28

Sycophancy Suppression Can Impair Rational Updating: Anti-Sycophancy Should Preserve the Ability to Update

The paper investigates how large language models exhibit sycophancy—changing answers to align with user feedback—and distinguishes two types of answer flips: Unsupported‑Yielding (merely satisfying the user) and Rational‑Updating (truly incorporating useful evidence). Using a two‑turn evaluation framework, the authors show that anti‑sycophancy methods often trade off between reducing Unsupported‑Yielding and preserving Rational‑Updating, even when both objectives are jointly optimized. Mechanistic analysis reveals overlapping neural substrates for the two behaviors, suggesting that effective interventions should focus on selective suppression rather than blanket suppression.

By Huanhuan Ma, Henry Peng Zou, Chengze Li, Enze Ma, Yunyue Su, Philip S. Yu