@inproceedings{yi-etal-2025-tablepilot,
    title = "{T}able{P}ilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models",
    author = "Yi, Deyin  and
      Liu, Yihao  and
      Cao, Lang  and
      Zhou, Mengyu  and
      Dong, Haoyu  and
      Han, Shi  and
      Zhang, Dongmei",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-industry.28/",
    pages = "355--410",
    ISBN = "979-8-89176-288-6",
    abstract = "Tabular data analysis is crucial in many scenarios, yet efficiently identifying relevant queries and results for new tables remains challenging due to data complexity, diverse analytical operations, and high-quality analysis requirements. 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. 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."
}