MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits

TL;DR

MAB-DQA introduces a multi-armed bandit approach to improve document question answering by dynamically prioritizing query aspects, leading to significant performance gains on multiple benchmarks.

Contribution

It proposes a novel aspect-aware retrieval framework using multi-armed bandits to better utilize multiple implicit query aspects in multimodal DQA.

Findings

Achieves 5%-18% improvement over state-of-the-art methods.

Effectively models query aspect importance for better retrieval.

Enhances document understanding in multimodal DQA.

Abstract

Document Question Answering (DQA) involves generating answers from a document based on a user's query, representing a key task in document understanding. This task requires interpreting visual layouts, which has prompted recent studies to adopt multimodal Retrieval-Augmented Generation (RAG) that processes page images for answer generation. However, in multimodal RAG, visual DQA struggles to utilize a large number of images effectively, as the retrieval stage often retains only a few candidate pages (e.g., Top-4), causing informative but less visually salient content to be overlooked in favor of common yet low-information pages. To address this issue, we propose a Multi-Armed Bandit-based DQA framework (MAB-DQA) to explicitly model the varying importance of multiple implicit aspects in a query. Specifically, MAB-DQA decomposes a query into aspect-aware subqueries and retrieves an aspect-specific candidate set for each. It treats each subquery as an arm and uses preliminary reasoning results from a small number of representative pages as reward signals to estimate aspect utility. Guided by an exploration-exploitation policy, MAB-DQA dynamically reallocates retrieval budgets toward high-value aspects. With the most informative pages and their correlations, MAB-DQA generates the expected results. On four benchmarks, MAB-DQA shows an average improvement of 5%-18% over the state-of-the-art method, consistently enhancing document understanding. Codes are available at https://github.com/ElephantOH/MAB-DQA.