What question did this study set out to answer?

The paper examines if large language models can genuinely hold beliefs or intentions.

March 25, 2026Open Access

AI, LLMs, and the normativity of belief

Puntos clave

The paper examines if large language models can genuinely hold beliefs or intentions.
Analyzes existing views on the nature and norms of belief.
Categorizes views into three types: denial of norms, mere constitutivism, and normativism.
Uses philosophical arguments to assess LLM capabilities in relation to belief.
Argues that if mere constitutivist or normativist views are accurate, LLMs cannot possess beliefs.
Distinguishes between different philosophical perspectives on belief norms.

Resumen

Whether or not large language models (LLMs) can be said to have representational attitudes like beliefs (or motivational attitudes like intentions) remains an open question. In this paper I argue that on some commonly accepted views about belief, LLMs, given their structure, are not capable of having beliefs. To do so, I draw from the normativity of belief literature to distinguish three types of views about the kinds of things beliefs are. The first category of view includes those which deny that there are any norms of belief at all, in any sense. The second category of views are those which allow that there might exist, in some way or another, a norm of belief, but hold that this is only true in a trivial or purely conceptual sense. I will call these the mere constitutivist accounts. The third category of views are any that suggest a stronger normative picture for the norm of belief. I’ll refer to these views as normativism about belief. My hope is to show that if either the mere constituivist or normativist accounts about belief are correct, then LLMs are not capable of having attitudes like beliefs or intentions.

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