CRAFT: Training-Free Cascaded Retrieval for Tabular QA

Le résumé fourni par la source

Open-Domain Table Question Answering (TQA) involves retrieving relevant tables from a large corpus to answer natural language queries.Traditional dense retrieval models, such as DTR and DPR, not only incur high computational costs for large-scale retrieval tasks but also require retraining or finetuning on new datasets, limiting their adaptability to evolving domains and knowledge.In this work, we propose CRAFT, a zeroshot, cascaded retrieval approach that first uses a sparse retrieval model to filter a subset of candidate tables before applying more computationally expensive dense models as rerankers.To improve retrieval quality, we enrich table representations with descriptive titles and summaries generated by Gemini Flash 1.5, enabling richer semantic matching between queries and tabular structures.Our method outperforms state-of-the-art (SOTA) sparse, dense, and hybrid retrievers on the NQ-Tables dataset.It also demonstrates strong zero-shot performance on the more challenging OTT-QA benchmark, achieving competitive results at higher recall thresholds, where the task requires multi-hop reasoning across both textual passages and relational tables.This work establishes a scalable and adaptable paradigm for table retrieval, bridging the gap between fine-tuned architectures and lightweight, plug-and-play retrieval systems.

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