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Recent advances in multimodal foundation models like GPT-4V have shown strong performance in general visual and textual data tasks. However, adapting these models to specialized domains like biomedicine requires large, domain-specific instruction datasets. While automatic dataset generation has been explored, these datasets often need more alignment with expert knowledge, limiting their real-world applicability. Instruction tuning, which fine-tunes models using task-specific prompts, has been effective but relies on extensive, costly datasets. Challenges include the lack of publicly available data generators and limited clinician-annotated data, hindering the development of expert-aligned models for specialized applications.
Researchers from Stanford University and Harvard Medical School have developed a framework called Biomedical Visual Instruction Tuning with Clinician Preference Alignment (BioMed-VITAL). This data-centric approach integrates clinician preferences in generating and selecting instruction data for biomedical multimodal foundation models. Initially, clinician-selected demonstrations guide the generation of relevant data using GPT-4V. Subsequently, a selection model, informed by clinician-annotated and model-annotated data, ranks the generated samples based on quality. The framework significantly enhances model performance, achieving an 18.5% improvement in open visual chat and an 81.73% win rate in biomedical visual question answering.
Instruction tuning has become a powerful technique for adapting pre-trained language models to various natural language tasks by providing task-specific instructions and examples. Notable studies like FLANT5, LLaMA, and LLaMA2 have demonstrated its effectiveness without extensive fine-tuning. Recent approaches suggest using robust language models to automatically generate high-quality instruction data, enabling cost-effective training, as seen with Stanford Alpaca’s use of text-davinci-003 to instruction-tune LLaMA. Adapting vision-language models poses challenges in the biomedical field due to limited training data. This work aims to create a data-centric method that aligns clinician expertise with instructional data for improved instruction tuning.
The BioMed-VITAL framework for clinician-aligned biomedical visual instruction tuning consists of three stages: data generation, data selection, and instruction tuning. In the first stage, diverse expert-selected demonstrations are used with the GPT-4V model to create an instructional dataset. The second stage involves training a data selection model that distills clinician preferences from human annotations and model-based evaluations to filter out low-quality samples. Finally, in the instruction tuning phase, the curated dataset adapts a general multimodal model for biomedical tasks, enhancing its performance through targeted learning on clinician-relevant data.
The study on BioMed-VITAL generated multi-round QA instructional data from image-text pairs in the PMC-15M dataset using the GPT-4 vision API and BiomedCLIP. Instruction tuning employed the llava-v1.5-13b model to enhance alignment with clinician preferences. The optimal training data mixture was a ratio of 1:400 between human and model preferences, achieving peak performance at a weight of 400. BioMed-VITAL outperformed the LLaVA-Med baseline in open-ended medical visual chat evaluations, excelling in accuracy and recall across benchmarks like VQA-RAD, SLAKE, and PathVQA, demonstrating the effectiveness of incorporating clinician preferences in data generation and selection.
In conclusion, the study presents BioMed-VITAL, a data-centric framework designed for biomedical visual instruction tuning that aligns closely with clinician preferences. By integrating clinician expertise into data generation and selection processes, BioMed-VITAL creates high-quality datasets that enhance the performance of visual instruction tuning models in biomedicine. The generation phase utilizes a variety of clinician-selected demonstrations to guide the GPT-4V generator. In contrast, the selection phase involves a dedicated model that refines clinician preferences to identify the most relevant data. This approach leads to notable improvements in downstream tasks, with a significant performance increase in open visual chat and medical visual question answering.
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“}]] [[{“value”:”Recent advances in multimodal foundation models like GPT-4V have shown strong performance in general visual and textual data tasks. However, adapting these models to specialized domains like biomedicine requires large, domain-specific instruction datasets. While automatic dataset generation has been explored, these datasets often need more alignment with expert knowledge, limiting their real-world applicability. Instruction tuning,
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