TablePilot：利用大语言模型推荐符合人类偏好的表格数据分析
TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models
AI总结：
TablePilot利用大语言模型自动推荐表格数据分析的查询-代码-结果三元组，通过Rec-Align对齐人类偏好，在DART数据集上实现77.0%的top-5召回率。
摘要：
表格数据分析在许多场景中至关重要，然而，为新表格高效识别最相关的数据分析查询和结果仍然是一个重大挑战。表格数据的复杂性、多样化的分析操作以及对高质量分析的需求使得这一过程变得繁琐。为了应对这些挑战，我们旨在为表格数据分析工作流中的新表格推荐量身定制的查询-代码-结果三元组。在本文中，我们提出了TablePilot，一个开创性的表格数据分析框架，利用大语言模型自主生成全面且优越的分析结果，无需依赖用户画像或先前的交互。该框架整合了分析准备和分析优化中的关键设计，以提升准确性。此外，我们提出了Rec-Align，一种新颖的方法，以进一步提高推荐质量并更好地与人类偏好对齐。在DART（一个专门为全面表格数据分析推荐而设计的数据集）上的实验证明了我们框架的有效性。基于GPT-4o，经过调优的TablePilot实现了77.0%的top-5推荐召回率。人工评估进一步突显了其在优化表格数据分析工作流方面的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
