Silkie: Preference Distillation for Large Visual Language Models

Lei Li, Xie, Mukai Li, Shunian Chen, PEIYI, WANG, Liang Chen, Yazheng Yang, Wang, Lingpeng Kong

Silkie: Preference Distillation for Large Visual Language Models: 10 upvotes on Hugging Face Daily Papers, #9 of 17 papers on 2023-12-19. Day-by-day upvote history.

This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual context. We first build a vision-language feedback (VLFeedback) dataset utilizing AI annotation. Specifically, responses are generated by models sampled from 12 LVLMs, conditioned on multi-modal instructions sourced from various datasets. We adopt GPT-4V to assess the generated outputs regarding helpfulness, visual faithfulness, and ethical considerations. Furthermore, the preference supervision is distilled into Qwen-VL-Chat through the direct preference optimization (DPO) method. The resulting model Silkie, achieves 6.9% and 9.5% relative improvement on the MME benchmark regarding the perception and cognition capabilities, respectively. Silkie also demonstrates reduced hallucination by setting a new state-of-the-art score of 3.02 on the MMHal-Bench benchmark. Further analysis shows that DPO with our VLFeedback dataset mainly boosts the fine-grained perception and complex cognition abilities of LVLMs, leading to more comprehensive improvements compared to human-annotated preference datasets.

Paper page on Hugging Face · arXiv

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