October 3, 2025Open Access

WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification

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Generated MCoTs improved interpretability by 37%, enhancing user understanding of image classifications.
Through ten datasets, classification accuracy increased, demonstrating WISE's applicability across varied contexts.
Weak-supervision-guided explanations bridge concept-based interpretability with generative reasoning for MLLMs.
This framework enables more refined visual understanding, addressing common gaps in existing MCoT approaches.

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Implication

Weak-supervision-guided explanations improve interpretability and classification accuracy in multimodal settings, suggesting enhanced understanding of intra-object relationships.

Cite This Study

Jiang et al. (2025) studied this question.

synapsesocial.com/papers/68e0450fa99c246f578b3f48 https://doi.org/10.48550/arxiv.2509.17740

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Implication

Weak-supervision-guided explanations improve interpretability and classification accuracy in multimodal settings, suggesting enhanced understanding of intra-object relationships.

WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification

Key Points

Discussion

Implication

Cite This Study

WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification

Key Points

Discussion

Implication

Cite This Study

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