Beyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering

TL;DR

This paper introduces JETRTQA, a multimodal learning framework that enhances document retrieval relevance in textbook question answering by joint training with ranking supervision, leading to improved accuracy.

Contribution

The paper presents a novel joint training approach with ranking supervision for multimodal document retrieval in TQA, outperforming previous methods.

Findings

Achieves 2.4% accuracy gain on validation set

Achieves 11.1% accuracy gain on test set

Improves discrimination between relevant and irrelevant documents

Abstract

Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances have improved overall performance, they often encounter difficulties in educational settings where accurate semantic alignment and task-specific document retrieval are essential. In this paper, we propose a novel approach to multimodal textbook question answering by introducing a mechanism for enhancing semantic representations through multi-objective joint training. Our model, Joint Embedding Training With Ranking Supervision for Textbook Question Answering (JETRTQA), is a multimodal learning framework built on a retriever--generator architecture that uses a retrieval-augmented generation setup, in which a multimodal large language model generates answers. JETRTQA is designed to improve the relevance of retrieved documents in complex educational contexts. Unlike traditional direct scoring approaches, JETRTQA learns to refine the semantic representations of questions and documents through a supervised signal that combines pairwise ranking and implicit supervision derived from answers. We evaluate our method on the CK12-QA dataset and demonstrate that it significantly improves the discrimination between informative and irrelevant documents, even when they are long, complex, and multimodal. JETRTQA outperforms the previous state of the art, achieving a 2.4\% gain in accuracy on the validation set and 11.1\% on the test set.