AI RESEARCH
Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
arXiv CS.CL
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ArXi:2509.23782v4 Announce Type: replace While large language models (LLMs) perform strongly on diverse tasks, their trustworthiness is limited by erratic behavior that is unfaithful to their internal knowledge. In particular, LLMs often fail on multiple-choice questions (MCQs) even if they encode correct answers in their hidden representations, revealing a misalignment between internal knowledge and output behavior. We investigate and mitigate this knowledge-prediction gap on MCQs through a three-step analysis of hidden representations.