PromptNCE: Pointwise Mutual Information Predictions Using Only LLMs and Contrastive Estimation Prompts
arXiv:2605.21776v1 Announce Type: new 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 augment
The rapid advancement of large language models makes it timely to explore their capabilities for foundational tasks like mutual information estimation, moving beyond traditional statistical methods.
This development could significantly lower the barrier to entry for complex information theoretic analyses, making advanced AI techniques more accessible for researchers and developers even in low-data environments.
The ability of LLMs to estimate pointwise mutual information zero-shot, using only prompts, changes the paradigm for how such estimations are performed, potentially obviating the need for task-specific critics.
- · AI researchers
- · NLP developers
- · Low-data domains
- · LLM providers
- · Traditional statistical model developers
- · Specialized information theory software
Researchers can now more easily conduct information-theoretic analyses using existing large language models.
This could accelerate research in areas requiring mutual information estimation, such as causal inference, feature selection, and anomaly detection.
The methodology might extend to other complex statistical estimations, further expanding the 'zero-shot' capabilities of LLMs and reducing reliance on specialized domain expertise.
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Read at arXiv cs.CL