MuDABench provides 332 analytical QA instances over large semi-structured document collections, showing standard RAG performs poorly while a multi-agent workflow with planning, extraction, and code generation improves results but leaves a gap to human experts.
TASK:You are asked to consider each part of the model output separated by ¡next chunk¿ as a piece of information extracted by the model, and compute:
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Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA
MuDABench provides 332 analytical QA instances over large semi-structured document collections, showing standard RAG performs poorly while a multi-agent workflow with planning, extraction, and code generation improves results but leaves a gap to human experts.