SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information
Jiashuo Sun, Jihai Zhang, Yucheng Zhou, Zhaochen Su, Xiaoye Qu, Yu Hua Cheng · 2024
Large Vision-Language Models (LVLMs) have become pivotal at the intersection of computer vision and natural language processing.However, the full potential of LVLMs' Retrieval-Augmented Generation (RAG) capabilities remains underutilized.Existing works either focus solely on the text modality or are limited to specific tasks.Moreover, most LVLMs struggle to selectively utilize retrieved information and are sensitive to irrelevant or misleading references.To address these challenges, we propose a self-refinement framework designed to teach LVLMs to Selectively Utilize Retrieved Information (SURf).Specifically, when given questions that are incorrectly answered by the LVLM backbone, we obtain references that help correct the answers (positive references) and those that do not (negative references).We then fine-tune the LVLM backbone using a combination of these positive and negative references.Our experiments across three tasks and seven datasets demonstrate that our framework significantly enhances LVLMs' ability to effectively utilize retrieved multimodal references and improves their robustness against irrelevant or misleading information.The source code is available at https://github.com/GasolSun36/SURf. * Work done during internship at Shanghai AI Laboratory.† Both are corresponding authors.How many apples in the images? VQAVanilla: There are three apples.The image depicting five apples on a tree...The picture shows 7 apples .... leaves...Ours: There are four apples.Describe this image in details. Captioning Vanilla: A person walking in snow.The image depicting a...the skier is in a crouched position... The image captures a dynamic scene ..a skier dressed in a ...