Papers by Daniel Jeong

2 papers
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? (2024.emnlp-main)

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Challenge: Several studies claim that domain-adaptive pretraining improves performance on downstream medical tasks.
Approach: They compare medical LLMs and VLMs against their corresponding base models . they find that medical Lms outperform their base models in 12.1% of cases .
Outcome: The proposed models outperform their base models on medical questions and tasks in 12.1% of cases and reach a tie in 49.8% of cases.
BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum (2025.findings-emnlp)

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Challenge: Large Language Models (LLMs) struggle with proactive engagement, authors say . a blind clinical evaluation confirmed that trained agents exhibit more realistic clinical behavior .
Approach: They propose a training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection.
Outcome: The proposed training strategy boosts performance on both benchmarks.

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