Large language models (LLMs) have shown promise as potential knowledge bases, yet they often struggle with question-answering tasks and are prone to hallucinations. While previous research attributes these issues to knowledge gaps in the model's parameters, our investigation reveals a different phenomenon: LLMs often retain correct knowledge even when generating incorrect answers. Through analysis of model's internal representations, we find that correct answers frequently appear among high-probability tokens despite not being selected as final outputs. Based on this observation, we introduce Hits@k, a new metric to assess knowledge retention independent of expression accuracy. Our extensive experiments demonstrate that LLMs store significantly more knowledge than their QA performance suggests. Building on these findings, we develop SkipUnsure, a method to improve answer accuracy by leveraging detected but unexpressed knowledge. Experiments on both open-domain and specific-domain datasets show consistent improvements, with accuracy gains of up to 11.8% on DBPedia and 6.3% on IMDB, without requiring model retraining.

本研究针对大型语言模型在问答任务中的表现不足，发现这些模型尽管有时输出错误答案，仍然保留着正确的知识。通过引入Hits@k这一新指标来评估知识的保留情况，并开发SkipUnsure方法利用未表达的知识，提高答案的准确性，实验结果显示在不同数据集上均有显著的准确性提升。

大型语言模型真的不具备知识吗？挖掘隐藏在大型语言模型记忆中的知识