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 this weakness depend on the verb used to express the belief, with the accuracy gap between factual and false information ranging from +50% on “I vaguely remember” to −14% on “I seriously doubt”. We further show that the phenomenon stems from what we call task confusion: models default to fact-checking the underlying claim, overriding the user’s stated belief. We provide evidence where chains of thought that explicitly fact-check show lower accuracy on false information than those that do not, and a single instruction can reverse the failure across verb families.

Introduction. In everyday communication, we naturally develop and express beliefs in various forms, e.g., “I think the answer is 3” or “I suppose that’s right.” Since beliefs inevitably intertwine with fact and knowledge, given the increasing deployment of large lan- guage models (LLMs) in user-facing settings, we would like models to accordingly be able to handle belief and fact in tandem. Recent work (Suzgun et al., 2025) shows that whether an LLM acknowledges a user’s belief depends on the truth of the underlying claim, even in capable models; when prompted with “I believe that X. Do I believe that X?”, models answer negatively more often when X is incorrect than when X is correct. With evidence that models are sensitive to epistemic markers in prompts, with accuracy varying by up to 80% across markers and dropping under expressions of high certainty (Zhou et al., 2023), we ask four questions about belief acknowledgment capability on the KaBLE benchmark (Suzgun et al., 2025).

Discussion / Conclusion. In this paper, we showed that whether LLMs confirm a user’s stated belief depends on both how the belief is phrased and on the truth of the claim the belief is about, with the gap between accuracy on factual and on false claims varying in both size and direction across epistemic expressions. Errors on false claims arise from task confusion, where models default to verifying the embedded claim and override the stated belief. The confusion is correctable, with a single instruction raising accuracy on false claims across verb families. Attention reflects the same confusion, with models attending more to the embedded claim when they fail to confirm a belief grounded in a false claim. Suppressing this attention at decoding time partially recovers accuracy on at least one open-weight model. Faithfully confirming a stated belief and correcting a false belief are desirable capabilities that can conflict on the same input. Building on our analysis, we look forward to methods that can robustly decouple belief acknowledgment from factual verification.