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Familiarity-probe scores can effectively differentiate real entities from fabricated ones, revealing surprising insights into how language models gauge their knowledge before responding.
Entity familiarity and factual reliability in LLMs are distinct phenomena, with models showing high awareness of known entities yet rarely abstaining from incorrect answers.