用LLM规划计算步骤，再由Python精准算数，提升表格问答准确率。
Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering
- LLM只做推理规划，实际计算交由Python执行
- 在两个数据集上准确率分别提升5.7%和5.8%
- 首次量化答案可信度，支持用户感知后悔风险
自由格式表格问答（TableQA）因表格结构灵活、模式复杂而具有挑战性。现有研究使用大语言模型（LLMs）理解问题与表格数据，但忽视了数值字段的处理难题——而LLMs在数值计算上表现不佳。本文提出一种名为TabLaP的新模型，将LLM用作规划器而非答案生成器，利用其多步推理能力，将实际数值计算交给Python解释器完成，确保精度。同时，首次尝试量化TabLaP输出答案的可信度，使用户能以“后悔感知”方式使用系统。在两个基准数据集上的实验表明，相比现有最优模型，该方法在准确率上分别提升了5.7%和5.8%。
原文摘要 · Abstract (English)
Question answering on free-form tables (a.k.a. TableQA) is a challenging task because of the flexible structure and complex schema of tables. Recent studies use Large Language Models (LLMs) for this task, exploiting their capability in understanding the questions and tabular data, which are typically given in natural language and contain many textual fields, respectively. While this approach has shown promising results, it overlooks the challenges brought by numerical values which are common in tabular data, and LLMs are known to struggle with such values. We aim to address this issue, and we propose a model named TabLaP that uses LLMs as a planner rather than an answer generator. This approach exploits LLMs' capability in multi-step reasoning while leaving the actual numerical calculations to a Python interpreter for accurate calculation. Recognizing the inaccurate nature of LLMs, we further make a first attempt to quantify the trustworthiness of the answers produced by TabLaP, such that users can use TabLaP in a regret-aware manner. Experimental results on two benchmark datasets show that TabLaP is substantially more accurate than the state-of-the-art models, improving the answer accuracy by 5.7% and 5.8% on the two datasets, respectively.
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