提前转换表格为标准格式，让模型更稳定地回答复杂问题。
QUIETT: Query-Independent Table Transformation for Robust Reasoning
- 先于查询处理表格，统一格式并修复结构缺陷。
- 在五个模型族上提升问答准确率，尤其对新问题效果显著。
- 适合需要可靠表格推理的智能系统开发人员。
真实世界表格常存在模式不一致、值格式异构及隐含关系结构等问题，影响表格推理与问答效果。现有方法在查询时处理这些问题，重复进行归一化和重构，且结果难以泛化到未见查询。我们提出QuIeTT，一种先转换、后查询的框架：在任何查询出现前，将每张原始表格转化为单一的可执行SQL的标准表示，遵循三大约束：无查询访问、信息完整保留、单表复用。QuIeTT采用三阶段流程：问题探测（通过合成查询揭示模糊模式与格式异构）、计划生成、结构化执行。在四个基准上，覆盖五个模型家族的实验表明，无论直接提示还是代理式推理，均持续优于强基线，尤其在人工标注的结构多样未见问题集上表现突出。
原文摘要 · Abstract (English)
Real-world tables often contain schema inconsistencies, heterogeneous value formats, and implicit relational structures that degrade table reasoning and question answering. Existing approaches address these issues at query time, repeating normalization and restructuring for every new question and producing representations that do not generalize to unseen queries. We introduce QuIeTT, a transform-first, query-later framework that converts each raw table into a single SQL-ready canonical representation before any evaluation query is observed, under three constraints: no evaluation query access, information preservation, and single-table reuse. QuIeTT operates through a three-stage pipeline: issue probing, where synthetic queries surface structural deficiencies such as ambiguous schemas and heterogeneous formats; plan generation; and structured execution. Experiments on four benchmarks across five model families show consistent improvements over strong baselines in both direct prompting and agentic reasoning paradigms, with particularly strong gains on a manually annotated challenge set of structurally diverse unseen questions.
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