[2603.12702] FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning

Computer Science > Information Retrieval arXiv:2603.12702 (cs) [Submitted on 13 Mar 2026 (v1), last revised 30 Mar 2026 (this version, v2)] Title:FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning Authors:Chaojie Sun, Bin Cao, Tiantian Li, Chenyu Hou, Ruizhe Li, Jing Fan View a PDF of the paper titled FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning, by Chaojie Sun and 5 other authors View PDF HTML (experimental) Abstract:With the rapid advancement of large language models (LLMs), growing efforts have been made on LLM-based table retrieval. However, existing studies typically focus on single-table query, and implement it by similarity matching after encoding the entire table. These methods usually result in low accuracy due to their coarse-grained encoding which incorporates much query-irrelated data, and are also inefficient when dealing with large tables, failing to fully utilize the reasoning capabilities of LLM. Further, multi-table query is under-explored in retrieval tasks. To this end, we propose a hierarchical multi-table query method based on LLM: Fine-Grained Multi-Table Retrieval FGTR, a new retrieval paradigm that employs a human-like reasoning strategy. Through hierarchical reasoning, FGTR first identifies relevant schema elements and then retrieves the corresponding cell contents, ultimately constructing a concise and accurate sub-table that aligns with the given query. To comprehensively evaluate the performance of...