priors

This paper studies why language models hallucinate, proposing that hallucinations often stem from biased latent inference (inference misalignment) rather than missing knowledge. It introduces TrapQA, a controlled diagnostic testbed to test reasoning against priors, and demonstrates that hallucinations can arise from misleading latent associations.

This paper investigates the ability of LLMs-as-judges for safety to adapt to contextual information and varying safety definitions, finding that they are largely rigid and fail to adjust when the context contradicts their internal priors.

This paper investigates how LLMs' internal priors affect zero-shot annotation performance, finding that nearly two-thirds of errors resist prompt-based correction and introducing Definition-Specific Familiarity as a better predictor than memorization metrics.