Whether LLMs Can Navigate Beliefs and Facts Depends on How You Phrase It
Humans naturally form and express beliefs in daily communication, e.g., "I think the answer is 3" or "I suppose that's right." Such beliefs inevitably intertwine with fact and knowledge, making the ability to handle them in tandem desirable for large language models (LLMs), as they are increasingly deployed in user-facing settings. Prior work showed that even capable LLMs exhibit a systemic weakness in acknowledging user beliefs grounded in incorrect information. We extend this evaluation to 10 LLMs across 18 epistemic expressions and find that the size and direction of the weakness depend on
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- FuzzyOverlapping authors or contributors · 62%open-webui/open-webui →
“Shared author/contributor keys: nguyen”
- PossiblePossibly related (embedding) · 48%LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs’ Probabilistic Beliefs - Apple Machine Learning Research →
- LinkedLinked via arxiv author · 85%Quang Minh Nguyen →
“Whether LLMs Can Navigate Beliefs and Facts Depends on How You Phrase It”
- LinkedLinked via arxiv author · 85%Luis Frentzen Salim →
“Whether LLMs Can Navigate Beliefs and Facts Depends on How You Phrase It”