Papers by Jiayuan Ding

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.
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)

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Challenge: Existing knowledge graphs are far from complete with large portions of triplets missing.
Approach: They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance.
Outcome: The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought.

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