Title: Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Instruction-free tuning, Instruction tuning, Visual instruction tuning, Large vision language models, Medical image analysis

Abstract: Large vision language models (LVLMs) have demonstrated impressive performance across various tasks, but struggle in domains with limited data, such as medicine. While visual instruction tuning addresses this by fine-tuning models with instruction-image-output triplets, constructing large-scale and high-quality datasets remains challenging in domains requiring expert knowledge. To address this, we introduce an instruction-free tuning that reduces reliance on handcrafted or auto-generated instructions, leveraging only image-output pairs during fine-tuning. Specifically, we propose a momentum proxy instruction as a replacement for explicit instructions, preserving the instruction-following capability of the pre-trained LVLM while promoting refined updates for parameters that remain valid during inference. Consequently, the fine-tuned LVLM can flexibly respond to domain-specific instructions, even when explicit instructions are absent during fine-tuning. Additionally, we incorporate a response shuffling strategy to mitigate the model's over-reliance on previous words, facilitating more effective fine-tuning. Our approach achieves state-of-the-art accuracy on multiple-choice visual question answering tasks across SKINCON, WBCAtt, and CBIS datasets, significantly enhancing fine-tuning efficiency in medical domains.

TL;DR: We introduce and formalize instruction-free tuning, a novel paradigm for fine-tuning LVLMs that eliminates the need for handcrafted or auto-generated instructions, addressing a key bottleneck in adapting models to specialized domains.