DuQUAD：面向多文档问答的双视图框架与质量感知证据剪枝
DuQUAD: A Dual-View Framework with Quality-Aware Evidence Pruning for Multi-Document Question Answering
AI 导读
研究者提出 DuQUAD，通过双视图推理和质量感知证据剪枝缓解大语言模型检索增强生成中的 Lost in the Middle 位置偏差。框架结合基于 Structural Fusion Score 的 Local Agent 和对全文档集推理的 Global Agent，并用质量评分过滤、句子级精炼候选证据。
正文 · 原文
Abstract:Large language models with retrieval-augmented generation systematically underutilize evidence in the middle of multi-document contexts, a phenomenon known as Lost in the Middle. Existing mitigation strategies, such as document reordering and attention steering, can reduce boundary bias but remain brittle in noisy settings, where inaccurate relevance estimation and spurious documents still obscure useful evidence in central positions. We propose DuQUAD, a multi-document QA framework that mitigates positional bias through dual-view reasoning and quality-aware evidence pruning. DuQUAD combines a Local Agent guided by a Structural Fusion Score with a Global Agent that reasons over the full document set, providing both targeted evidence recovery and broad contextual understanding. The framework further filters candidate evidence using explicit quality scoring and refines it at the sentence level to reduce noise. DuQUAD consistently outperforms strong Lost-in-the-Middle mitigation baselines, including hybrid retrieval and reranking, multi-agent reasoning, and context optimization methods, improving accuracy by 3.0% on average over the strongest competing baseline and gold evidence recall by 9.4%, while substantially reducing computational cost compared to existing multi-agent frameworks.
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.37701 [cs.IR] |
| (or arXiv:2610.37701v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37701 arXiv-issued DOI via DataCite |
Submission history
From: Yeonjun Choi [view email]
[v1]
Sun, 25 Oct 2026 14:14:11 UTC (484 KB)
来源：arXiv cs.AI · arxiv.org