CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering

Shaowei Wang, Lingling Zhang, Longji Zhu, Tao Qin, Kim-Hui Yap, Xinyu Zhang, Jun Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 13969-13979

Diagram Question Answering (DQA) is a challenging task requiring models to answer natural language questions based on visual diagram contexts. It serves as a crucial basis for academic tutoring technical support and more practical applications. DQA poses significant challenges such as the demand for domain-specific knowledge and the scarcity of annotated data which restrict the applicability of large-scale deep models. Previous approaches have explored external knowledge integration through pre-training but these methods are costly and can be limited by domain disparities. While Large Language Models (LLMs) show promise in question-answering there is still a gap in how to cooperate and interact with the diagram parsing process. In this paper we introduce the Chain-of-Guiding Learning Model for Diagram Question Answering (CoG-DQA) a novel framework that effectively addresses DQA challenges. CoG-DQA leverages LLMs to guide diagram parsing tools (DPTs) through the guiding chains enhancing the precision of diagram parsing while introducing rich background knowledge. Our experimental findings reveal that CoG-DQA surpasses all comparison models in various DQA scenarios achieving an average accuracy enhancement exceeding 5% and peaking at 11% across four datasets. These results underscore CoG-DQA's capacity to advance the field of visual question answering and promote the integration of LLMs into specialized domains.