用检索增强生成技术，让大模型高效理解百万级表格数据
TableRAG: Million-Token Table Understanding with Language Models
- 通过查询扩展与模式/单元格检索，精准定位关键表格信息
- 在百万级表格上实现最优检索质量，性能超越现有方法
- 适合需要处理超大规模结构化数据的AI应用开发者
近期语言模型在处理表格数据方面取得显著进展，主要依赖程序辅助机制进行表格操作与分析。然而，这些方法通常需将整张表作为输入，受限于位置偏差或上下文长度，存在可扩展性问题。为此，我们提出TableRAG——一种专为基于语言模型的表格理解设计的检索增强生成框架。TableRAG结合查询扩展与模式、单元格检索，在输入语言模型前精准定位关键信息，提升编码效率并减少信息丢失。我们基于Arcade和BIRD-SQL数据集构建了两个新的百万级令牌基准测试集，全面评估TableRAG在大规模场景下的表现。结果表明，TableRAG的检索设计实现了最高检索质量，在大规模表格理解任务中达到新基准性能。
原文摘要 · Abstract (English)
Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables. However, these methods often require the entire table as input, leading to scalability challenges due to the positional bias or context length constraints. In response to these challenges, we introduce TableRAG, a Retrieval-Augmented Generation (RAG) framework specifically designed for LM-based table understanding. TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information before providing it to the LMs. This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss. We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale. Our results demonstrate that TableRAG's retrieval design achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding.
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