Microsoft AI Unveils LLaVA-Med: An Efficiently Trained Large Language and Vision Assistant Revolutionizing Biomedical Inquiry, Delivering Advanced Multimodal Conversations in Under 15 Hours

      

There is a lot of potentials for conversational generative AI to help medical professionals, but so far, the research has only focused on text. While advances in multi-modal conversational AI have been rapid because of billions of publicly available image-text pairings, such general-domain vision-language models still need more complexity when interpreting and chatting about biological pictures. The research team at Microsoft suggests a low-effort method for teaching a vision-language conversational assistant to respond to free-form inquiries about biomedical images. The team proposes a novel curriculum learning approach to the fine-tuning of a large general-domain vision-language model using a large-scale, broad-coverage biomedical figure-caption dataset extracted from PubMed Central and GPT-4 to self-instruct open-ended instruction-following data from the captions.

The model mimics the progressive process by which a layman gains biological knowledge by initially learning to align biomedical vocabulary using the figure-caption pairs as-is and then learning to master open-ended conversational semantics using GPT-4 generated instruction-following data. In less than 15 hours (with eight A100s), researchers can train a Large Language and Vision Assistant for BioMedicine (LLaVA-Med). With its multi-modal conversational capacity and ability to follow free-form instructions, LLaVA-Med is well-suited to answering questions regarding biological images. Fine-tuned LLaVA-Med achieves state-of-the-art performance on three benchmark biomedical visual question-answering datasets. The data on how well people follow directions and the LLaVA-Med model will be made public to advance multi-modal research in biomedicine.

The team’s key contributions are summed up as follows:

Multi-modal medical training compliance statistics. By selecting biomedical picture-text pairs from PMC-15M and running GPT-4 to generate instructions from the text alone, they describe a unique data creation pipeline to generate diverse (image, instruction, output) instances.

LLaVA-Med. Using the self-generated biomedical multi-modal instruction-following dataset, they offer a novel curriculum learning method to adapt LLaVA to the biomedical domain.

Open-source. The biomedical multi-modal instruction-following dataset and the software for data generation and model training will be publicly available to promote further study in biomedical multi-modal learning.

The effectiveness of LLaVA-Med and the accuracy of the multi-modal biomedical instruction-following data obtained were the focus of the team’s investigations. Researchers look at two different contexts for evaluating research:

How effective is LLaVA-Med as a general-purpose biomedical visual chatbot?

Compared to the state-of-the-art methodologies, how does LLaVA-Med fare on industry benchmarks?

The team first proposes a novel data generation pipeline that samples 600K image-text pairs from PMC-15M, curates diverse instruction-following data through GPT-4, and aligns the created instructions to the model to solve the problem of a lack of multi-modal biomedical datasets for training an instruction-following assistant.

Researchers then introduce a new method of teaching LLaVA-Med’s curriculum. Specifically, they train the LLaVA multi-modal conversation model in broad domains and gradually shift their focus to the biomedical field. There are two phases to the training process:

Specification of a Biomedical Idea Word embeddings is aligned with the relevant image attributes of a large set of innovative biological visual concepts.

With its fine-tuned model based on biomedical language-image instructions, LLaVA-Med shows impressive zero-shot task transfer capabilities and facilitates natural user interaction.

To sum it up

The research team at Microsoft provides LLaVA-Med, a large language and vision model for the biomedical field. They use a self-instruct strategy to construct a data curation pipeline with language-only GPT-4 and external knowledge. Then they train the model on a high-quality biomedical language-image instruction-following dataset. LLaVA-Med beats earlier supervised SoTA on three VQA datasets on specific measures after fine-tuning, demonstrating great conversation abilities with domain knowledge. While LLaVA-Med is a big step in the right direction, they also recognize that it has hallucinations and a lack of depth of reasoning that plague many LMMs. Future initiatives will be towards making things more reliable and high-quality.

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