PEARL: Front-Loading Relational Chains for Multi-Hop Table Retrieval

• URL: http://arxiv.org/abs/2608.30291v1
• Date: Mon, 31 Aug 2026 05:56:40 GMT
• Title: PEARL: Front-Loading Relational Chains for Multi-Hop Table Retrieval
• Abstract summary: PEARL is a training-free framework that shifts the paradigm toward vertical partitioning-based sub-table encoding.<n>PEARL augments the retrieval corpus offline by generating multi-hop queries over pre-identified join paths.<n>Experiments show that PEARL consistently outperforms existing methods, with up to +30.05% gains in R@2 on 3-hop queries.
• Score: 15.811717493688812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While large language models (LLMs) have shown strong capabilities in tabular reasoning, retrieving relevant tables remains challenging due to the fragmented and relational structure of real-world data. Existing work typically relies on whole table representations that overlook cross-table semantics induced by join relationships. We propose PEARL, a training-free framework that shifts the paradigm toward vertical partitioning-based sub-table encoding. PEARL augments the retrieval corpus offline by generating multi-hop queries over pre-identified join paths and reorganizing relevant columns into vertically partitioned corpus units, enabling effective multi-table retrieval without query-time LLM inference. Experiments show that PEARL consistently outperforms existing methods, with up to +30.05% gains in R@2 on 3-hop queries. The source code is available at https://github.com/SOOB2NHO/PEARL.

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