Model Merging for Medical LVLMs: A Benchmark and a Winner-Take-All Approach
Merged summary
TL;DR - The first systematic study of model merging for medical large vision-language models (LVLMs), introducing a benchmark and a simple "winner-take-all" method that consolidates multiple LoRA fine-tuned experts into one model without retraining—cutting deployment overhead while beating existing merging approaches.
- MergeMedBench: a new benchmark spanning 8 imaging modalities and diverse clinical tasks, with 16 LoRA fine-tuned expert models built on two mainstream LVLM architectures.
- Problem: LoRA yields many modality/task-specific expert LVLMs, but deploying them all is computationally and operationally costly; merging avoids retraining but was unexplored in medicine.
- Winner-take-all method: hyperparameter-free approach that keeps only the most dominant parameters across experts, discarding weaker ones to avoid the "information dilution" of averaging/alignment-based merging.
- Result (as claimed): consistently outperforms existing merging methods, positioning it as a strong practical baseline for LoRA merging; specific metrics are not given in the provided abstract.
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Model Merging for Medical LVLMs: A Benchmark and a Winner-Take-All Approach
TL;DR - The first systematic study of model merging for medical large vision-language models (LVLMs), introducing a benchmark and a simple "winner-take-all" method that consolidates multiple LoRA fine-tuned experts into one model without retraining—cutting deployment overhead while beating existing merging approaches.
- MergeMedBench: a new benchmark spanning 8 imaging modalities and diverse clinical tasks, with 16 LoRA fine-tuned expert models built on two mainstream LVLM architectures.
- Problem: LoRA yields many modality/task-specific expert LVLMs, but deploying them all is computationally and operationally costly; merging avoids retraining but was unexplored in medicine.
- Winner-take-all method: hyperparameter-free approach that keeps only the most dominant parameters across experts, discarding weaker ones to avoid the "information dilution" of averaging/alignment-based merging.
- Result (as claimed): consistently outperforms existing merging methods, positioning it as a strong practical baseline for LoRA merging; specific metrics are not given in the provided abstract.