Assertion and Belief: Moore's Paradox in Language Models
We present the first test of Moore's paradox in LLMs, evaluating 12 models on a curated set of Moorean sentences in varied contexts.
ProofPaper ↗
Key points
- Moore's paradox \citep{moore1942} concerns sentences such as "It is raining, but I don't believe that it is," which may well be true yet cannot be sensibly asserted.
- This makes the paradox a test of whether language models take the speech act into account when computing meaning.
- We find that models largely recognize the infelicity of Moorean sentences, and that the judgement is not tied to a memorized form and tracks the speech act.
- We find the models' own listeners do not reason about their own speakers, and their reasoning traces suggest that they instead recall knowledge about the paradox and apply rules therein.
Sources (1)
- [1]Assertion and Belief: Moore's Paradox in Language ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 11:32 PM
We present the first test of Moore's paradox in LLMs, evaluating 12 models on a curated set of Moorean sentences in varied contexts.
Moore's paradox \citep{moore1942} concerns sentences such as "It is raining, but I don't believe that it is," which may well be true yet cannot be sensibly asserted.
Extractive summary: sentences quoted from the sources.
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