MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tables

• URL: http://arxiv.org/abs/2502.11735v2
• Date: Tue, 18 Feb 2025 05:12:25 GMT
• Title: MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tables
• Authors: Kwangwook Seo, Donguk Kwon, Dongha Lee,
• Abstract summary: MT-RAIG Bench is designed to evaluate systems on Retrieval-Augmented Insight Generation over Mulitple-Tables.<n>We introduce a fine-grained evaluation framework MT-RAIG Eval, which better alignment with human quality judgments on the generated insights.
• Score: 11.268174270952489
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in table-based reasoning have expanded beyond factoid-level QA to address insight-level tasks, where systems should synthesize implicit knowledge in the table to provide explainable analyses. Although effective, existing studies remain confined to scenarios where a single gold table is given alongside the user query, failing to address cases where users seek comprehensive insights from multiple unknown tables. To bridge these gaps, we propose MT-RAIG Bench, design to evaluate systems on Retrieval-Augmented Insight Generation over Mulitple-Tables. Additionally, to tackle the suboptimality of existing automatic evaluation methods in the table domain, we further introduce a fine-grained evaluation framework MT-RAIG Eval, which achieves better alignment with human quality judgments on the generated insights. We conduct extensive experiments and reveal that even frontier LLMs still struggle with complex multi-table reasoning, establishing our MT-RAIG Bench as a challenging testbed for future research.

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