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This is a Quantitative Study study.

September 29, 2025Open Access

Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs

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Authors

HFHao Fang CZChangle ZhouXiamen University JKJiawei KongTsinghua University

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Implication

This method demonstrates reduced hallucinations in LVLMs by improving mutual dependency between image and text, suggesting enhanced decoding efficacy.

Key Points

Significantly reduces hallucinations in LVLMs while maintaining decoding efficiency, improving response accuracy.
Utilizes a token purification mechanism to regulate text and image token relevance for better output.
Innovative approach involves a bi-level optimization problem maximizing mutual information between modalities.
Extensive experiments across various benchmarks validate the effectiveness of the proposed method.

Cite This Study

Fang et al. (2025) studied this question.

synapsesocial.com/papers/68da5a3ec1728099cfd11a6f https://doi.org/10.48550/arxiv.2505.19678

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Implication

This method demonstrates reduced hallucinations in LVLMs by improving mutual dependency between image and text, suggesting enhanced decoding efficacy.

Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs

Discussion

Member takes

Implication

Key Points

Cite This Study

Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs

Discussion

Member takes

Implication

Key Points

Cite This Study

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