As large language models (LLMs) continue to demonstrate remarkable abilities
across various domains, computer scientists are developing methods to
understand their cognitive processes, particularly concerning how (and if) LLMs
internally represent their beliefs about the world. However, this field
currently lacks a unified theoretical foundation to underpin the study of
belief in LLMs. This article begins filling this gap by proposing adequacy
conditions for a representation in an LLM to count as belief-like. We argue
that, while the project of belief measurement in LLMs shares striking features
with belief measurement as carried out in decision theory and formal
epistemology, it also differs in ways that should change how we measure belief.
Thus, drawing from insights in philosophy and contemporary practices of machine
learning, we establish four criteria that balance theoretical considerations
with practical constraints. Our proposed criteria include accuracy, coherence,
uniformity, and use, which together help lay the groundwork for a comprehensive
understanding of belief representation in LLMs. We draw on empirical work
showing the limitations of using various criteria in isolation to identify
belief representations.

大语言模型 (Large Language Models) 内部如何表达其对世界的信念是一个缺乏统一理论基础的研究领域，本文通过提出适当性条件，填补了这一空白。研究人员通过结合哲学和机器学习方法，建立了准确度、连贯性、一致性和应用等四个标准，以促进对大语言模型中信念表达的全面理解。