Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA

• URL: http://arxiv.org/abs/2604.22239v1
• Date: Fri, 24 Apr 2026 05:28:51 GMT
• Title: Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA
• Abstract summary: This paper introduces the task of analytical question answering over large, semi-structured document collections.<n>We present MuDABench, a benchmark for multi-document analytical QA.
• Score: 25.155696504567718
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces the task of analytical question answering over large, semi-structured document collections. We present MuDABench, a benchmark for multi-document analytical QA, where questions require extracting and synthesizing information across numerous documents to perform quantitative analysis. Unlike existing multi-document QA benchmarks that typically require information from only a few documents with limited cross-document reasoning, MuDABench demands extensive inter-document analysis and aggregation. Constructed via distant supervision by leveraging document-level metadata and annotated financial databases, MuDABench comprises over 80,000 pages and 332 analytical QA instances. We also propose an evaluation protocol that measures final answer accuracy and uses intermediate-fact coverage as an auxiliary diagnostic signal for the reasoning process. Experiments reveal that standard RAG systems, which treat all documents as a flat retrieval pool, perform poorly. To address these limitations, we propose a multi-agent workflow that orchestrates planning, extraction, and code generation modules. While this approach substantially improves both process and outcome metrics, a significant gap remains compared to human expert performance. Our analysis identifies two primary bottlenecks: single-document information extraction accuracy and insufficient domain-specific knowledge in current systems. MuDABench is available at https://github.com/Zhanli-Li/MuDABench.

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