Papers by Siting Li

2 papers
Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models (2025.findings-emnlp)

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Challenge: Rapid medical concept drift can lead LLMs to provide incorrect or outdated advice.
Approach: They propose to evaluate how large language models manage knowledge conflicts in clinical guidelines.
Outcome: The proposed benchmark evaluates how LLMs manage varied knowledge conflicts in clinical guidelines.
Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder (2025.acl-long)

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Challenge: Recent studies show that CLIP models struggle with visual reasoning tasks . despite the success of Contrastive Language-Image Pretraining, there are still limitations .
Approach: They propose to use a visual encoder to train CLIP-like models for fine-grained visual reasoning tasks.
Outcome: The proposed models outperform CLIP-like encoders in visual reasoning tasks . the study highlights the importance of VLM architectural choices .

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