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On the attribution of confidence to large language models

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abstract

Credences are mental states corresponding to degrees of confidence in propositions. Attribution of credences to Large Language Models (LLMs) is commonplace in the empirical literature on LLM evaluation. Yet the theoretical basis for LLM credence attribution is unclear. We defend three claims. First, our semantic claim is that LLM credence attributions are (at least in general) correctly interpreted literally, as expressing truth-apt beliefs on the part of scientists that purport to describe facts about LLM credences. Second, our metaphysical claim is that the existence of LLM credences is at least plausible, although current evidence is inconclusive. Third, our epistemic claim is that LLM credence attributions made in the empirical literature on LLM evaluation are subject to non-trivial sceptical concerns. It is a distinct possibility that even if LLMs have credences, LLM credence attributions are generally false because the experimental techniques used to assess LLM credences are not truth-tracking.

fields

cs.AI 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Propositional Interpretability in Artificial Intelligence

cs.AI · 2025-01-27 · conditional · novelty 7.0

Chalmers proposes propositional interpretability, interpreting AI in terms of beliefs, desires, and credences, and sets the challenge of thought logging all such attitudes over time.

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  • Propositional Interpretability in Artificial Intelligence cs.AI · 2025-01-27 · conditional · none · ref 12 · internal anchor

    Chalmers proposes propositional interpretability, interpreting AI in terms of beliefs, desires, and credences, and sets the challenge of thought logging all such attitudes over time.