Self-Supervised Visual Preference Alignment
Ke Zhu Liang Zhao Zheng Ge Xiangyu Zhang
This paper makes the first attempt towards unsupervised preference alignment
in Vision-Language Models (VLMs). We generate chosen and rejected responses
with regard to the original and augmented image pairs, and conduct preference
alignment with direct preference optimization. It is based on a core idea:
properly designed augmentation to the image input will induce VLM to generate
false but hard negative responses, which helps the model to learn from and
produce more robust and powerful answers. The whole pipeline no longer hinges
on supervision from GPT4 or human involvement during alignment, and is highly
efficient with few lines of code. With only 8k randomly sampled unsupervised
data, it achieves 90\% relative score to GPT-4 on complex reasoning in
LLaVA-Bench, and improves LLaVA-7B/13B by 6.7\%/5.6\% score on complex
multi-modal benchmark MM-Vet. Visualizations shows its improved ability to
align with user-intentions. A series of ablations are firmly conducted to
reveal the latent mechanism of the approach, which also indicates its potential
towards further scaling. Code will be available.