Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA
MuDABench uses a multi-agent workflow for analytical QA across documents, significantly improving accuracy.
Key Findings
Methodology
MuDABench employs a multi-agent workflow comprising planning, extraction, and code generation modules. Each module handles distinct tasks like sub-query planning, single-document extraction, and JSON normalization, followed by programmatic analysis for final answer synthesis.
Key Results
- Experiments show traditional RAG systems perform poorly on large document sets, while the multi-agent workflow significantly improves final answer accuracy, reaching 80% of human expert level.
- In 332 analytical QA instances, systems using MuDABench improved accuracy by approximately 20% over standard RAG.
- Ablation studies reveal single-document extraction accuracy is a primary bottleneck for overall performance.
Significance
MuDABench holds significant value for academia and industry, addressing long-standing challenges in cross-document information aggregation, particularly in financial regulation and analysis.
Technical Contribution
Technical contributions include proposing a new multi-agent workflow that significantly enhances cross-document extraction and aggregation efficiency, offering new engineering possibilities like automated financial report analysis.
Novelty
MuDABench is the first to achieve analytical QA over large document collections, breaking existing systems' limitations in cross-document reasoning and providing a new evaluation protocol.
Limitations
- Single-document extraction accuracy remains a primary bottleneck, impacting overall performance.
- Current systems lack sufficient domain-specific knowledge, affecting complex problem-solving.
Future Work
Future work will focus on improving single-document extraction accuracy and enhancing domain-specific knowledge to further close the gap with human experts.
AI Executive Summary
MuDABench is a novel benchmark focused on analytical QA over large document collections. Existing systems perform poorly in cross-document information aggregation, while MuDABench significantly enhances performance in this domain through a multi-agent workflow.
The workflow includes planning, extraction, and code generation modules, each responsible for distinct tasks, enabling efficient information aggregation and analysis. Experimental results show MuDABench excels in 332 QA instances, significantly improving final answer accuracy.
Nevertheless, single-document extraction accuracy remains a primary bottleneck, and future work will focus on improving this aspect and enhancing domain-specific knowledge to further close the gap with human experts.
Deep Analysis
Background
In recent years, QA systems face challenges in handling multi-document information, with traditional methods often failing to effectively aggregate cross-document information. MuDABench addresses this issue through a multi-agent workflow, providing a new analytical QA framework.
Core Problem
Existing multi-document QA systems typically handle only a few documents, lacking cross-document reasoning capabilities. MuDABench aims to solve this problem by achieving complex analytical tasks through efficient information aggregation.
Innovation
MuDABench's core innovation lies in its multi-agent workflow, capable of efficiently handling analytical QA tasks over large document collections. Compared to existing methods, MuDABench significantly improves extraction and aggregation efficiency.
Methodology
- �� Planning Agent: Generates sub-query templates
- �� Extraction Agent: Single-document information extraction
- �� Normalization Agent: Converts results to JSON format
- �� Code Agent: Analyzes structured data and generates final answers
Experiments
Experiments used the MuDABench dataset, including 80,000 pages of documents and 332 QA instances. Baselines included standard RAG systems, with evaluation metrics being final answer accuracy and process accuracy.
Results
MuDABench significantly improved final answer accuracy in experiments, reaching 80% of human expert level. Ablation studies show single-document extraction is the primary bottleneck.
Applications
MuDABench can be used for financial report analysis and anomaly detection, helping regulators and enterprises efficiently aggregate and analyze information.
Limitations & Outlook
Single-document extraction accuracy needs improvement, and the system lacks sufficient domain-specific knowledge, affecting complex problem-solving. Future work will focus on these aspects.
Plain Language Accessible to non-experts
Imagine you're in a library trying to find out which companies changed their accounting firms in 2024. You need to extract information from thousands of reports and analyze them. It's like finding the right puzzle pieces in a huge puzzle and putting them together to complete the picture. MuDABench is the tool that helps you quickly find the right puzzle pieces and finish the puzzle.
ELI14 Explained like you're 14
Imagine you're playing a super complex game, and the goal is to find out which companies changed their accounting firms in 2024. You have a super smart assistant that can quickly browse through thousands of reports, find the key information, and tell you the result. This assistant is MuDABench, making you win the game effortlessly!
Glossary
MuDABench
MuDABench is a benchmark designed for multi-document analytical QA, containing 80,000 pages and 332 QA instances.
Used to evaluate cross-document information aggregation performance.
RAG System
RAG systems combine retrieval and generation techniques to extract information from documents and answer questions.
Used as a baseline in experiments.
Multi-Agent Workflow
A distributed processing framework comprising multiple agent modules responsible for planning, extraction, and analysis tasks.
Core methodology of MuDABench.
Intermediate Fact Coverage
Used to evaluate the accuracy of the information extraction process as an auxiliary diagnostic signal.
Helps analyze the reasoning process of the system.
Financial Dataset
Includes annual reports, ESG reports, and corporate announcements used for analytical QA tasks.
Data source for MuDABench.
Open Questions Unanswered questions from this research
- 1 How to improve single-document extraction accuracy remains an open question.
- 2 Current systems lack sufficient domain-specific knowledge, affecting complex problem-solving.
Applications
Immediate Applications
Financial Report Analysis
MuDABench can be used to analyze financial reports, helping regulators detect anomalies.
Enterprise Information Aggregation
Enterprises can use MuDABench for efficient information aggregation and analysis, optimizing decision processes.
Long-term Vision
Automated Financial Analysis
MuDABench has the potential to achieve automated financial analysis, reducing human intervention and increasing efficiency.
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.