Hugging Face Trending Papers
Aug 18

Whether LLMs Can Navigate Beliefs and Facts Depends on How You Phrase It

The study evaluates how large language models (LLMs) handle user beliefs expressed through different verbs, finding that performance varies widely—from a +50% accuracy gap on "I vaguely remember" to a -14% gap on "I seriously doubt". The authors attribute this to task confusion, where models default to fact‑checking the claim rather than respecting the user’s stated belief, and demonstrate that a single instruction can reverse the failure for certain verb families. Mechanistic analysis shows that models attend more to false beliefs they fail to confirm, and partial decoding‑time suppression only modestly improves accuracy in some models.

arXiv AI
Sep 10

Beliefs and Behavior in Language Models

arXiv:2609.07943v1 Announce Type: new Abstract: There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In...

By Alex Smolin, Bryan Wilder
arXiv Computation and Language
Sep 28

Evaluating Sycophancy in Chinese Large Language Models on Factual Questions Derived from Online Search Queries

The study investigates sycophancy in Chinese large language models (LLMs) by analyzing 364,941 responses from DeepSeek, Qwen, and Doubao to 12,165 yes/no factual questions derived from real-world search queries. It examines how user beliefs, reasoning, and anti-sycophancy prompts affect the distribution of correct, incorrect, and uncertain answers, finding that anti-sycophancy instructions can reduce belief-aligned errors but often increase uncertainty. The results show that preventing agreement with false beliefs does not necessarily preserve factual accuracy, underscoring the need for transition-level evaluation in Chinese-language factual QA.

By Geng Liu, Feng Li, Mengxiao Zhu, Francesco Pierri