@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://aclanthology.org/2025.acl-industry.28/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models
%A Yi, Deyin
%A Liu, Yihao
%A Cao, Lang
%A Zhou, Mengyu
%A Dong, Haoyu
%A Han, Shi
%A Zhang, Dongmei
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F yi-etal-2025-tablepilot
%X 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.
%R 10.18653/v1/2025.acl-industry.28
%U https://aclanthology.org/2025.acl-industry.28/
%U https://doi.org/10.18653/v1/2025.acl-industry.28
%P 355-410
Markdown (Informal)
[TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models](https://aclanthology.org/2025.acl-industry.28/) (Yi et al., ACL 2025)
ACL