Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning

• URL: http://arxiv.org/abs/2505.17436v1
• Date: Fri, 23 May 2025 03:31:58 GMT
• Title: Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning
• Authors: Cheng Peng, Kai Zhang, Mengxian Lyu, Hongfang Liu, Lichao Sun, Yonghui Wu,
• Abstract summary: We developed two vision language models, BiomedGPT-Large and BiomedGPT-XLarge, based on an encoder-decoder-based transformer architecture.<n>We fine-tuned the two models on 23 benchmark datasets from 6 multi-modal biomedical tasks.<n>We assessed the zero-shot learning performance and alignment accuracy.
• Score: 25.982757026324474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To advance biomedical vison-language model capabilities through scaling up, fine-tuning, and instruction tuning, develop vision-language models with improved performance in handling long text, explore strategies to efficiently adopt vision language models for diverse multi-modal biomedical tasks, and examine the zero-shot learning performance. We developed two biomedical vision language models, BiomedGPT-Large and BiomedGPT-XLarge, based on an encoder-decoder-based transformer architecture. We fine-tuned the two models on 23 benchmark datasets from 6 multi-modal biomedical tasks including one image-only task (image classification), three language-only tasks (text understanding, text summarization and question answering), and two vision-language tasks (visual question answering and image captioning). We compared the developed scaled models with our previous BiomedGPT-Base model and existing prestigious models reported in the literature. We instruction-tuned the two models using a large-scale multi-modal biomedical instruction-tuning dataset and assessed the zero-shot learning performance and alignment accuracy.

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