🤖 AI Summary
This work addresses the challenge of multi-hop question answering over hybrid table-text data, where retrieval noise significantly degrades performance and conventional RAG approaches struggle to model reasoning chains due to their flat document representation. The authors propose the first zero-shot graph-based framework that leverages large language models to dynamically construct an evidence graph from noisy retrieval results—treating documents as nodes and semantic relations as edges—to explicitly capture multi-hop reasoning paths and automatically identify bridging documents. Requiring no task-specific training, the method achieves 48.80 EM on OTT-QA, outperforming strong baselines by 19.9 points, matching the performance of fine-tuned retrieval models (CORE: 49.0 EM), and approaching that of the state-of-the-art system (COS: 56.9 EM).
📝 Abstract
Multi-hop question answering over hybrid table-text data requires retrieving and reasoning across multiple evidence pieces from large corpora, but standard Retrieval-Augmented Generation (RAG) pipelines process documents as flat ranked lists, causing retrieval noise to obscure reasoning chains. We introduce N2N-GQA. To our knowledge, it is the first zeroshot framework for open-domain hybrid table-text QA that constructs dynamic evidence graphs from noisy retrieval outputs. Our key insight is that multi-hop reasoning requires understanding relationships between evidence pieces: by modeling documents as graph nodes with semantic relationships as edges, we identify bridge documents connecting reasoning steps, a capability absent in list-based retrieval. On OTT-QA, graph-based evidence curation provides a 19.9-point EM improvement over strong baselines, demonstrating that organizing retrieval results as structured graphs is critical for multihop reasoning. N2N-GQA achieves 48.80 EM, matching finetuned retrieval models (CORE: 49.0 EM) and approaching heavily optimized systems (COS: 56.9 EM) without any task specific training. This establishes graph-structured evidence organization as essential for scalable, zero-shot multi-hop QA systems and demonstrates that simple, interpretable graph construction can rival sophisticated fine-tuned approaches.