PromptNCE: Pointwise Mutual Information Predictions Using Only LLMs and Contrastive Estimation Prompts
This paper proposes PromptNCE, which estimates pointwise mutual information (PMI) zero-shot using only LLMs and prompts, avoiding the need for task-specific critics. It achieves Spearman correlation up to 0.82 on three datasets and demonstrates applicability in low-data educational settings.
[2605.21776] PromptNCE: Pointwise Mutual Information Predictions Using Only LLMs and Contrastive Estimation Prompts
[Submitted on 20 May 2026]
Title:PromptNCE: Pointwise Mutual Information Predictions Using Only LLMs and Contrastive Estimation Prompts
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Abstract:Estimating mutual information from text usually requires training a task-specific critic, which limits its use in low-data settings. We ask whether large language models can instead estimate pointwise mutual information zero-shot, using only prompts and elicited probabilities. We introduce a benchmark with human-derived ground-truth PMI across three publicly available datasets, and evaluate five information-theoretic prompting-based estimators. Our main method, PromptNCE, frames conditional probability estimation as a contrastive task and augments the candidate set with an explicit OTHER category. We show theoretically that adding OTHER recovers the true conditional P(y | x) rather than just a ranking over listed candidates, turning a contrastive prompt into a general-purpose zero-shot probability estimator. PromptNCE is the best zero-shot method on all three datasets, reaching Spearman correlation up to 0.82 with human-derived PMI. We also present a case study in computer science education showing how these estimators can be used to score student knowledge summaries in a low-data setting.
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Computation and Language (cs.CL)
Cite as: arXiv:2605.21776 [cs.CL]
(or arXiv:2605.21776v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.21776
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Juliette Woodrow [view email] [v1] Wed, 20 May 2026 22:10:54 UTC (197 KB)
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