大型语言模型中知识可访问性的几何结构

中文摘要 AI 辅助

大型语言模型（LLMs）包含广泛的知识，但它们无法可靠地访问所有知识。我们通过知识可访问性来研究这个问题，该概念描述了查询所需的知识能否从模型中回忆出来。我们发现，在模型对查询本身的表征中，知识可访问性具有简单的几何结构，且这种结构在任何生成之前就已存在。更可访问的查询在表征空间中更接近中心，而可访问性较低的查询则更远离中心。这种几何结构揭示了一个知识边界，将更可访问的查询与较不可访问的查询区分开来。可访问性随与中心距离的增加而持续下降，并且这种基于距离的排序在不同数据集之间具有迁移性，即使中心不同也是如此。受控实验进一步表明，这种中心化几何结构与知识可访问性的关系比与推理难度的关系更密切。该几何结构还揭示了不同干预措施何时有用。查询重写对更可访问的查询帮助更大，思维链推理在边界附近帮助更大，而检索在边界之外带来更大的收益。这些发现不仅为语言模型中知识的组织方式提供了新的几何视角，还为自适应推理提出了一个有用的生成前信号。

英文摘要

Large language models (LLMs) contain broad knowledge, but they cannot access all of it reliably. We study this problem through knowledge accessibility, which describes whether the knowledge needed for a query can be recalled from the model. We find that knowledge accessibility has a simple geometric structure in the model's representation of the query alone, before any generation. More accessible queries are closer to a center in the representation space, while less accessible queries are farther away. This geometry reveals a knowledge boundary that separates more accessible queries from less accessible ones. Accessibility consistently decreases with distance from the center, and this distance-based ordering transfers across datasets even when the centers differ. Controlled experiments further show that the centered geometry is more closely related to knowledge accessibility than to reasoning difficulty. The geometry also reveals when different interventions are useful. Query rewriting helps more for accessible queries, chain-of-thought reasoning helps more near the boundary, and retrieval gives larger gains beyond the boundary. These findings not only provide a new geometric view of how knowledge is organized in language models, but also suggest a useful pre-generation signal for adaptive inference.

发表机构

• Imperial College London（帝国理工学院）

机构由 AI 辅助整理，请以论文原文为准。