Pith. sign in

REVIEW 3 cited by

On the attribution of confidence to large language models

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.08388 v1 pith:V33YIJMR submitted 2024-07-11 cs.AI cs.CL

classification cs.AIcs.CL
keywords credencescredenceattributionattributionsclaimconfidenceempiricalevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Propositional Interpretability in Artificial Intelligence

    cs.AI 2025-01 conditional novelty 7.0 of 10

    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.

  2. LitLLMs, LLMs for Literature Review: Are we there yet?

    cs.CL 2024-12 conditional novelty 6.0 of 10

    LLMs can draft plausible related-work sections when the task is decomposed into keyword-plus-embedding retrieval, attribution-verified reranking, and plan-based generation, but retrieval coverage remains below 10 perc...

  3. A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

    cs.CL 2024-12 conditional novelty 4.0 of 10

    A review that organizes LLM uncertainty quantification into token-level, self-verbalized, semantic-similarity, and mechanistic interpretability categories.

Pith tools