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MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering
Not provided in the abstract
Med-VQAprobingmultimodalrepresentation learning
2609.04336
Builder Relevance
7h ago80%
Abstract
MedProb is a lightweight probing framework that enhances medical visual question answering without the need for fine-tuning or complex systems.
Reality Card
Core Claim
MedProb recovers more answer-relevant signals than prompting and outperforms medical VLMs and agentic systems in multiple-choice Med-VQA tasks.
Method / Result
MedProb shows improved performance across multiple datasets, recovering substantially more relevant signals than traditional prompting methods.
Limitations
The study does not consistently demonstrate that medical adaptation improves linear decodability across all tested models.
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