用大模型自动推荐表格分析查询，更贴合人类偏好。
TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models
- 基于大模型自动生成查询-代码-结果三元组。
- 在DART数据集上达77.0%的顶5召回率。
- 适合需要快速生成高质量表格分析的用户。
表格数据分析在诸多场景中至关重要，但高效识别新表格最相关的分析查询与结果仍具挑战。表格数据复杂、分析操作多样且对质量要求高，导致流程繁琐。为此，我们提出TablePilot，一个创新的表格数据分析框架，利用大语言模型自主生成全面且优质的分析结果，无需依赖用户画像或历史交互。框架包含分析准备与优化的关键设计，提升准确性。此外，我们提出Rec-Align方法，进一步提升推荐质量并更好契合人类偏好。在专为综合性表格分析推荐设计的DART数据集上，基于GPT-4o的调优版TablePilot达到77.0%的顶5推荐召回率。人工评估也证实其能有效优化表格数据分析流程。
原文摘要 · Abstract (English)
Tabular data analysis is crucial in many scenarios, yet efficiently identifying the most relevant data analysis queries and results for a new table remains a significant challenge. The complexity of tabular data, diverse analytical operations, and the demand for high-quality analysis make the process tedious. To address these challenges, we aim to recommend query-code-result triplets tailored for new tables in tabular data analysis workflows. In this paper, we present TablePilot, a pioneering tabular data analysis framework leveraging large language models to autonomously generate comprehensive and superior analytical results without relying on user profiles or prior interactions. The framework incorporates key designs in analysis preparation and analysis optimization to enhance accuracy. Additionally, we propose Rec-Align, a novel method to further improve recommendation quality and better align with human preferences. Experiments on DART, a dataset specifically designed for comprehensive tabular data analysis recommendation, demonstrate the effectiveness of our framework. Based on GPT-4o, the tuned TablePilot achieves 77.0% top-5 recommendation recall. Human evaluations further highlight its effectiveness in optimizing tabular data analysis workflows.
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