On Epistemic Diversity in Large Language Models

2026-09-04

AI Summary

论文探讨了大型语言模型在支持知识密集型任务时存在的认知多样性问题，并提出了一种衡量该多样性的初步框架，指出当前模型倾向于缩小有效答案空间。

Abstract

Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In such settings, evaluation cannot be exhausted by accuracy or alignment alone. A system may give a correct answer while still narrowing users' access %to knowledge. to alternative valid answers, explanations, or reasoning routes. Drawing on the broader notion of epistemic diversity in philosophy and social epistemology, we formalize it in the context of LLMs as the range of valid answers, explanations, and reasoning routes that an LLM exposes to users. We argue that epistemic diversity is a useful evaluation dimension for settings where LLMs are used to support knowledge-intensive tasks. We propose a preliminary framework for conceptualizing and measuring epistemic diversity in LLMs, and operationalize it in two domains. We find that frontier LLMs often exhibit epistemic narrowness, repeatedly collapsing large valid answer spaces onto small canonical subsets. These findings suggest that LLM evaluation should move beyond accuracy-oriented paradigms and treat epistemic diversity as an important dimension of model capability.

Problem

• epistemic diversity
• large language models
• knowledge-intensive tasks
• valid answers

Innovation

• epistemic diversity
• large language models
• evaluation framework