Papers by Yibing Du

2 papers
MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits (2026.acl-long)

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Challenge: Document Question Answering (DQA) requires interpreting visual layouts, which has prompted recent studies to adopt multimodal Retrieval-Augmented Generation (RAG) that processes page images for answer generation.
Approach: They propose a multi-armed bandit-based DQA framework that explicitly models the varying importance of multiple implicit aspects in a query.
Outcome: The proposed framework shows an improvement of 5%-18% over the state-of-the-art method on four benchmarks.
Improving Multi-Agent Debate with Sparse Communication Topology (2024.findings-emnlp)

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Challenge: Existing approaches to multi-agent debates use a brute force algorithm, resulting in a computationally intensive process.
Approach: They propose to extend the multi-agent debate framework to multi-modal reasoning and alignment labeling tasks, showcasing its broad applicability and effectiveness.
Outcome: The proposed framework can achieve comparable or superior performance while significantly reducing computational costs.

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