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.

翻译：表格数据分析在许多场景中至关重要，然而，如何为新表格高效识别最相关的数据分析查询与结果仍是一项重大挑战。表格数据的复杂性、多样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。为应对这些挑战，我们的目标是为表格数据分析工作流中的新表格推荐定制化的查询-代码-结果三元组。本文提出TablePilot，一个开创性的表格数据分析框架，它利用大语言模型自主生成全面且优质的分析结果，而无需依赖用户画像或历史交互记录。该框架在分析准备与分析优化中融入了关键设计以提升准确性。此外，我们提出了Rec-Align这一新方法，以进一步提升推荐质量并更好地与人类偏好对齐。在DART（一个专为全面表格数据分析推荐而设计的数据集）上进行的实验证明了我们框架的有效性。基于GPT-4o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。