用树形结构分解表格，让大模型更好理解复杂数据表。
Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding
- 将大表格压缩分解成小块，构建分层树状结构。
- 在多个数据集上表现超越现有方法，准确率显著提升。
- 适合需要处理海量复杂表格的科研与工业场景。
表格作为跨领域的半结构化数据，其广泛存在与高价值亟需先进理解方法。尽管大语言模型在自然语言理解上表现出色，但在大规模表格数据上的应用仍面临表规模大、关系复杂的挑战。现有方法对小规模表格有效，却难以应对真实场景中大型互联表格所需的复杂推理。为此，我们提出「Tree-of-Table」，通过表格压缩与分解，将相关数据提炼重组为可管理形式，并构建分层的表树结构，支持树形推理。通过系统的表树执行流程，逐步解析推理链条以获得答案。在WikiTQ、TableFact、FeTaQA和BIRD等多个数据集上的实验表明，Tree-of-Table达到新基准，展现出卓越的效率与泛化能力。
原文摘要 · Abstract (English)
The ubiquity and value of tables as semi-structured data across various domains necessitate advanced methods for understanding their complexity and vast amounts of information. Despite the impressive capabilities of large language models (LLMs) in advancing the natural language understanding frontier, their application to large-scale tabular data presents significant challenges, specifically regarding table size and complex intricate relationships. Existing works have shown promise with small-scale tables but often flounder when tasked with the complex reasoning required by larger, interconnected tables found in real-world scenarios. To address this gap, we introduce "Tree-of-Table", a novel approach designed to enhance LLMs' reasoning capabilities over large and complex tables. Our method employs Table Condensation and Decomposition to distill and reorganize relevant data into a manageable format, followed by the construction of a hierarchical Table-Tree that facilitates tree-structured reasoning. Through a meticulous Table-Tree Execution process, we systematically unravel the tree-structured reasoning chain to derive the solutions. Experiments across diverse datasets, including WikiTQ, TableFact, FeTaQA, and BIRD, demonstrate that Tree-of-Table sets a new benchmark with superior performance, showcasing remarkable efficiency and generalization capabilities in large-scale table reasoning.
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