LLMs estimate mutual information with new PromptNCE method

Researchers have developed PromptNCE, a novel method that enables large language models to estimate pointwise mutual information (PMI) without requiring a separate critic model. This approach frames conditional probability estimation as a contrastive task, incorporating an 'OTHER' category to improve accuracy. PromptNCE achieves strong zero-shot performance, reaching a Spearman correlation of up to 0.82 with human-derived PMI on benchmark datasets. AI

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IMPACT Enables LLMs to estimate mutual information zero-shot, potentially simplifying knowledge assessment and analysis in low-data scenarios.

RANK_REASON The cluster contains an academic paper detailing a new method for estimating mutual information using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• Large language models
• PromptNCE

• paper
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