用分层推理实现细粒度多表检索，提升准确率与效率
FGTR: Fine-Grained Multi-Table Retrieval via Hierarchical LLM Reasoning
- 分层推理：先找相关字段，再提取单元格内容
- 在Spider和BIRD上分别提升F2指标18%和21%
- 适合需要精准表格查询的复杂任务场景
随着大语言模型的发展，基于LLM的表格检索受到关注。但现有方法多针对单表查询，采用整体编码后相似度匹配，因粗粒度编码引入大量无关信息，导致准确率低，且在处理大表时效率差，未能充分发挥LLM的推理能力。多表查询更是研究不足。为此，我们提出基于LLM的分层多表查询方法：细粒度多表检索FGTR，一种类人推理的新范式。通过分层推理，FGTR先识别相关模式元素，再检索对应单元格内容，最终构建与查询一致的精简子表。为全面评估性能，我们基于Spider和BIRD构建了两个新基准数据集。实验表明，FGTR优于现有最先进方法，在Spider上F2提升18%，在BIRD上提升21%，证明其在提升细粒度检索效果及增强下游表格任务端到端性能方面的有效性。
原文摘要 · Abstract (English)
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.
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