Can language models automate data wrangling?
Open accessAbstract
The automation of data science and other data manipulation processes depend on the integration and formatting of ‘messy’ data. Data wrangling is an umbrella term for these tedious and time-consuming tasks. Tasks such as transforming dates, units or names expressed in different formats have been challenging for machine learning because (1) users expect to solve them with short cues or few examples, and (2) the problems depend heavily on domain knowledge. Interestingly, large language models today (1) can infer from very few examples or even a short clue in natural language, and (2) can integrate vast amounts of domain knowledge. It is then an important research question to analyse whether language models are a promising approach for data wrangling, especially as their capabilities continue growing. In this paper we apply different variants of the language model Generative Pre-trained Transformer (GPT) to five batteries covering a wide range of data wrangling problems. We compare the effect of prompts and few-shot regimes on their results and how they compare with specialised data wrangling systems and other tools. Our major finding is that they appear as a powerful tool for a wide range of data wrangling tasks. We provide some guidelines about how they can be integrated into data processing pipelines, provided the users can take advantage of their flexibility and the diversity of tasks to be addressed. However, reliability is still an important issue to overcome.
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GPT-3 has been suggested as a tool for helping writers and content creators generate ideas and overcome writer's block, and as a means of automating the production of repetitive or time-consuming content tasks.
“However, GPT-3 has also been suggested as a tool for helping writers and content creators generate ideas and overcome writer's block (Duval et al., 2020), and as a means of automating the production of repetitive or time-consuming content tasks (Jaimovitch-López et al., 2022).”
From Chatting and cheating: Ensuring academic integrity in the era of ChatGPT (2024) · cites this paper as Jaimovitch-López et al., 2022
The Introduction (page unavailable in our copy) describes data wrangling as repetitive and time-consuming and discusses automating those tasks with language models. The paper supports the automation part of the claim, but its tasks concern data preparation, not the production of content for writers or content creators. The writer’s-block clause is cited to a different source in the citing sentence. (LLM, conf=0.86)
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Cirac, G., & Pimenta, T. C. (2024) · doi:10.1109/icm63406.2024.10815866
Improving English language proficiency through text-based activities with the help of ChatGPT
Enamorado, M. Á. J. (2025) · Nordic Journal of Digital Literacy · doi:10.18261/njdl.20.1.8 · Cited by 1 · see citing works
End-To-End ML with LLMs and Semantic Data Management: Experiences from Chemistry 4.0
Hoseini, S., Herrmann, V., & Quix, C. (2025) · Workshop on Data Management for End-to-End Machine Learning (DEEM) · doi:10.1145/3735654.3735942
Polymer: Development Workflows as Software
Parthasarathy, D., Yu, Y., & Barr, E. T. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728494
LLM-Driven Compliance Checking for Natural-Language Policies over Data Product Descriptors
Simbola, F., Recupero, D. R., Riboni, D., & Salis, M. (2026) · Machine Learning · doi:10.1007/s10994-026-07038-6
Visualizing spatio-temporal-semantic coupling in social media user data
Huang, Y., Ye, J., Chen, S., & Zhong, Z. (2026) · Journal of Visualization · doi:10.1007/s12650-026-01141-5
Uyar, A. (2026) · Journal of Learning and Teaching in Digital Age · doi:10.53850/joltida.1825745
Wang, S., Yu, Y., Barr, E. T., & Parthasarathy, D. (2026) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3803437.3805251
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Organização e Arquitetura da Informação em Large Language Models
Procópio, D. C., & Silva, P. N. (2026) · Informação & Informação · doi:10.5433/1981-8920.2026v31n1p149
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Large Language Models Prompting for Multi-source Entity Matching Confirmation: A Promising Direction
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- Authors
- Jaimovitch-López, G., Ferri, C., Hernández-Orallo, J., Martínez-Plumed, F., & Ramírez-Quintana, M. J.
- Affiliations
- Universitat Politècnica de València (ES)
- Venue
- Machine Learning
- Published
- 2022-12-01
- Volume
- 112
- Issue
- 6
- Pages
- 2053-2082
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W4310529830
- Cited by
- 48
- References
- 44
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 16 · 2023: 7 · 2022: 1
- FWCI
- 3.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Machine Learning (publishedVersion, cc-by)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Funders
- Future of Life Institute (RFP2-152)
- MIT-Spain - INDITEX Sustainability Seed Fund (COST-OMIZE)
- Ministerio de Economía, Industria y Competitividad, Gobierno de España (RTI2018-094403-B-C32)
- Generalitat Valenciana (PROMETEO/2019/098, NNEST/2021/317)
- Horizon 2020 (952215 (TAILOR))
- Defense Advanced Research Projects Agency (HR00112120007 ReCOG-A)
- Universidad Politècnica de València
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- data wrangling, large language models, GPT, prompt engineering, few-shot learning, data integration, data transformation, data pipelines
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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