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    <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</abstract>
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%0 Conference Proceedings
%T PEARL: Front-Loading Relational Chains for Multi-Hop Table Retrieval
%A Ho, Subeen
%A Kang, Hyeongu
%A Kang, SeongKu
%A Yoon, Susik
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ho-etal-2026-pearl
%X 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
%U https://aclanthology.org/2026.emnlp-main.2549/
%P 57952-57976

Markdown (Informal)

[PEARL: Front-Loading Relational Chains for Multi-Hop Table Retrieval](https://aclanthology.org/2026.emnlp-main.2549/) (Ho et al., EMNLP 2026)

• PEARL: Front-Loading Relational Chains for Multi-Hop Table Retrieval (Ho et al., EMNLP 2026)

ACL

• Subeen Ho, Hyeongu Kang, SeongKu Kang, and Susik Yoon. 2026. PEARL: Front-Loading Relational Chains for Multi-Hop Table Retrieval. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57952–57976, Budapest, Hungary. Association for Computational Linguistics.