提出迭代搜索方法，让机器更快更准地从多张表中找可连接的答案。
Exploring Multi-Table Retrieval Through Iterative Search
- 用迭代搜索替代复杂优化，兼顾相关性、覆盖度和表间可连接性
- 在5个测试集上表现接近最优解，速度提升4到400倍
- 适合需要快速处理海量表格的开放域问答系统
在数据湖上进行开放域问答需要从多张表中检索并组合信息，这一任务具有挑战性，要求语义相关性和结构一致性（如可连接性）。虽然混合整数规划（MIP）等精确优化方法能保证一致性，但计算开销过大。相反，仅优化查询覆盖率的简单贪心启发式方法常无法找到一致且可连接的表集合。本文将多表检索建模为一种迭代搜索过程，认为该方法在可扩展性、可解释性和灵活性上具有优势。我们提出一个通用框架及具体实现：一种快速有效的贪心连接感知检索算法，综合平衡相关性、覆盖率与可连接性。在5个NL2SQL基准上的实验表明，该迭代方法在检索性能上可媲美基于MIP的方法，同时速度提升4至400倍，具体取决于基准和搜索空间设置。本工作凸显了迭代启发式方法在实际、可扩展、可组合检索中的潜力。
原文摘要 · Abstract (English)
Open-domain question answering over datalakes requires retrieving and composing information from multiple tables, a challenging subtask that demands semantic relevance and structural coherence (e.g., joinability). While exact optimization methods like Mixed-Integer Programming (MIP) can ensure coherence, their computational complexity is often prohibitive. Conversely, simpler greedy heuristics that optimize for query coverage alone often fail to find these coherent, joinable sets. This paper frames multi-table retrieval as an iterative search process, arguing this approach offers advantages in scalability, interpretability, and flexibility. We propose a general framework and a concrete instantiation: a fast, effective Greedy Join-Aware Retrieval algorithm that holistically balances relevance, coverage, and joinability. Experiments across 5 NL2SQL benchmarks demonstrate that our iterative method achieves competitive retrieval performance compared to the MIP-based approach while being 4-400x faster depending on the benchmark and search space settings. This work highlights the potential of iterative heuristics for practical, scalable, and composition-aware retrieval.
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