Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2511.21708 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908677519179776 |
|---|---|
| author | Spreafico, Matteo Tassini, Ludovica Sancricca, Camilla Cappiello, Cinzia |
| author_facet | Spreafico, Matteo Tassini, Ludovica Sancricca, Camilla Cappiello, Cinzia |
| contents | Large language models have recently demonstrated their exceptional capabilities in supporting and automating various tasks. Among the tasks worth exploring for testing large language model capabilities, we considered data preparation, a critical yet often labor-intensive step in data-driven processes. This paper investigates whether large language models can effectively support users in selecting and automating data preparation tasks. To this aim, we considered both general-purpose and fine-tuned tabular large language models. We prompted these models with poor-quality datasets and measured their ability to perform tasks such as data profiling and cleaning. We also compare the support provided by large language models with that offered by traditional data preparation tools. To evaluate the capabilities of large language models, we developed a custom-designed quality model that has been validated through a user study to gain insights into practitioners' expectations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21708 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lost in the Pipeline: How Well Do Large Language Models Handle Data Preparation? Spreafico, Matteo Tassini, Ludovica Sancricca, Camilla Cappiello, Cinzia Computation and Language Artificial Intelligence Large language models have recently demonstrated their exceptional capabilities in supporting and automating various tasks. Among the tasks worth exploring for testing large language model capabilities, we considered data preparation, a critical yet often labor-intensive step in data-driven processes. This paper investigates whether large language models can effectively support users in selecting and automating data preparation tasks. To this aim, we considered both general-purpose and fine-tuned tabular large language models. We prompted these models with poor-quality datasets and measured their ability to perform tasks such as data profiling and cleaning. We also compare the support provided by large language models with that offered by traditional data preparation tools. To evaluate the capabilities of large language models, we developed a custom-designed quality model that has been validated through a user study to gain insights into practitioners' expectations. |
| title | Lost in the Pipeline: How Well Do Large Language Models Handle Data Preparation? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2511.21708 |