Comparison of Visual Reliance Score in Multimodal Foundation Models Fine-Tuned on Synthetic vs. Authentic Image-Text Pairs for

Abstract

Abstract: Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.Our early experime

Research Question

How does the Visual Reliance Score (VRS) compare between multimodal foundation models fine-tuned on synthetic versus authentic image-text pairs across different medical VQA benchmarks?

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