Papers by Binxu Li

2 papers
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)

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Challenge: Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models.
Approach: They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs.
Outcome: The proposed agent performs better than open-source models and the closed-source model, GPT-4o.
LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers (2025.findings-acl)

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Challenge: Current slice discovery methods in computer vision rely on converting input images into sets of attributes and testing hypotheses about configurations of pre-computed attributes associated with elevated error patterns.
Approach: They propose a method to identify systematic biases in the mistakes of pre-trained vision models by converting input images into sets of attributes and testing hypotheses about configurations of these attributes.
Outcome: The proposed method outperforms existing methods on 3 natural and 3 medical imaging datasets and generates pseudo-labels for each identified bias.

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