Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering
Yuxiang Wang, Jianzhong Qi, Junhao Gan

TL;DR
This paper introduces TabLaP, a novel table question answering model that combines LLMs for multi-step reasoning with a Python interpreter for precise numerical calculations, and assesses answer trustworthiness.
Contribution
It proposes a new approach using LLMs as planners and a Python interpreter for calculations, improving accuracy and enabling regret-aware answer confidence estimation.
Findings
TabLaP outperforms state-of-the-art models with 5.7% and 5.8% accuracy improvements.
The model effectively combines reasoning and precise numerical computation.
Trustworthiness estimation allows regret-aware decision making.
Abstract
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…
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Taxonomy
TopicsTopic Modeling · AI-based Problem Solving and Planning · Intelligent Tutoring Systems and Adaptive Learning
