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

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 FGTR, we construct two new benchmark datasets based on Spider and BIRD . Experimental results show that FGTR outperforms previous state-of-the-art methods, improving the F_2 metric by 18% on Spider and 21% on BIRD, demonstrating its effectiveness in enhancing fine-grained retrieval and its potential to improve end-to-end performance on table-based downstream tasks.

Figures (5)

• Figure 1: Illustration of the human cognitive process for table comprehension and the motivation for fine-grained retrieval. (a) Humans typically adopt a hierarchical approach: they first scan schemas to identify relevant columns and then locate the specific rows needed, effectively creating a mental sub-table. (b) Performance comparison on BirdQA demonstrating the necessity of retrieval. Feeding the compact, retrieved sub-table significantly boosts QA accuracy, whereas feeding the full table leads to context window overflow and catastrophic failure.
• Figure 2: The overall architecture of our FGTR framework, which consists of an offline preprocessing phase and a two-stage online retrieval process: Schema Retrieval and Cell Retrieval.
• Figure 3: Performance comparison of FGTR and TableRAG across different table sizes. The x-axis represents table size (number of tokens). The left y-axis and the bar chart show the distribution of table sizes in our datasets. The right y-axis corresponds to the line plots, which compare the EM accuracy of FGTR against TableRAG on four downstream tasks. Each subplot corresponds to a different dataset.
• Figure 4: Oriainal tables from BirdQA with FGTR relecance heat-map. In the schema retrieval phase, deeper orange indicates higher frequency fields, while in the cell retrieval phase, green intensity distinguishes between constrained (dark) and dependent (light) cells.
• Figure 5: Original tables from WTQ with FGTR relecance heat-map.