Hidden Factual Knowledge in LLMs
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The curator’s take
This study introduces a framework to measure hidden knowledge in LLMs, showing that models encode significantly more factual information internally than they express in outputs, up to 40% more. It also finds that some answers, although known internally, are never generated, highlighting key limits in test-time sampling for QA tasks.
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