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%2C that incorporates clinician preferences into both stages of generating and selecting instruction data for tuning biomedical multimodal foundation models. First%2C during the generation stage%2C we prompt the GPT-4V generator with a diverse set of clinician-selected demonstrations for preference-aligned data candidate generation. Then%2C during the selection phase%2C we train a separate selection model%2C which explicitly distills clinician and policy-guided model preferences into a rating function to select high-quality data for medical instruction tuning. Results show that the model tuned with the instruction-following data from our method demonstrates a significant improvement in open visual chat (18.5%25 relatively) and medical VQA (win rate up to 81.73%25). Our instruction-following data and models are available at BioMed-VITAL.github.io.?quality=80&w=800)
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