PromptNCE frames LLM conditional probability estimation as contrastive prompting augmented with an OTHER category, recovering true P(y|x) and achieving up to 0.82 Spearman correlation with human-derived PMI on three datasets.
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MPD reduces hallucinations in LVLMs by 23.4% while retaining 97.4% of general capability through semantic disentanglement and selective parameter updates.
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PromptNCE: Pointwise Mutual Information Predictions Using Only LLMs and Contrastive Estimation Prompts
PromptNCE frames LLM conditional probability estimation as contrastive prompting augmented with an OTHER category, recovering true P(y|x) and achieving up to 0.82 Spearman correlation with human-derived PMI on three datasets.
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Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
MPD reduces hallucinations in LVLMs by 23.4% while retaining 97.4% of general capability through semantic disentanglement and selective parameter updates.