FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning
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 ret...
| Authors | Chaojie Sun, Bin Cao, Tiantian Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 13 March 2026 |
| Last updated at source | 30 March 2026 |
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