Empowering Tabular Data Preparation with Language Models: Why and How?
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918112345980928 |
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| author | Chen, Mengshi Sun, Yuxiang Li, Tengchao Wang, Jianwei Wang, Kai Lin, Xuemin Zhang, Ying Zhang, Wenjie |
| author_facet | Chen, Mengshi Sun, Yuxiang Li, Tengchao Wang, Jianwei Wang, Kai Lin, Xuemin Zhang, Ying Zhang, Wenjie |
| contents | Data preparation is a critical step in enhancing the usability of tabular data and thus boosts downstream data-driven tasks. Traditional methods often face challenges in capturing the intricate relationships within tables and adapting to the tasks involved. Recent advances in Language Models (LMs), especially in Large Language Models (LLMs), offer new opportunities to automate and support tabular data preparation. However, why LMs suit tabular data preparation (i.e., how their capabilities match task demands) and how to use them effectively across phases still remain to be systematically explored. In this survey, we systematically analyze the role of LMs in enhancing tabular data preparation processes, focusing on four core phases: data acquisition, integration, cleaning, and transformation. For each phase, we present an integrated analysis of how LMs can be combined with other components for different preparation tasks, highlight key advancements, and outline prospective pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01556 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empowering Tabular Data Preparation with Language Models: Why and How? Chen, Mengshi Sun, Yuxiang Li, Tengchao Wang, Jianwei Wang, Kai Lin, Xuemin Zhang, Ying Zhang, Wenjie Artificial Intelligence 68T50 I.2.7 Data preparation is a critical step in enhancing the usability of tabular data and thus boosts downstream data-driven tasks. Traditional methods often face challenges in capturing the intricate relationships within tables and adapting to the tasks involved. Recent advances in Language Models (LMs), especially in Large Language Models (LLMs), offer new opportunities to automate and support tabular data preparation. However, why LMs suit tabular data preparation (i.e., how their capabilities match task demands) and how to use them effectively across phases still remain to be systematically explored. In this survey, we systematically analyze the role of LMs in enhancing tabular data preparation processes, focusing on four core phases: data acquisition, integration, cleaning, and transformation. For each phase, we present an integrated analysis of how LMs can be combined with other components for different preparation tasks, highlight key advancements, and outline prospective pipelines. |
| title | Empowering Tabular Data Preparation with Language Models: Why and How? |
| topic | Artificial Intelligence 68T50 I.2.7 |
| url | https://arxiv.org/abs/2508.01556 |