Using Large Language Models for Data Cleaning: An Evaluation of ChatGPT-4o’s Performance
23rd Annual International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2025, Athens, Greece, 4 - 06 July 2025, vol.328, pp.121-125, (Full Text)
- Publication Type: Conference Paper / Full Text
- Volume: 328
- Doi Number: 10.3233/shti250685
- City: Athens
- Country: Greece
- Page Numbers: pp.121-125
- Keywords: ChatGPT, Data Cleaning, Data Quality, Large Language Models (LLMs)
- Akdeniz University Affiliated: Yes
Abstract
Data cleaning has a significant role in improving data quality. Although manual data cleaning is possible, it is a time-consuming and error-prone method that highlights the need for automated data cleaning approaches. ChatGPT is one of the tools that may be used to automate the data cleaning process. In the present study, we aimed to evaluate the performance of ChatGPT-4o in data cleaning. According to the study results, ChatGPT-4o achieved mean accuracies of 94.3%, 92.5%, 92.8%, and 70.0% in cleaning the gender, hemoglobin, route, and urine glucose variables, respectively. Accuracy was consistent across three trials for gender, hemoglobin, and route variables. However, significant variation was observed across trials for the urine glucose variable. While the findings of the study emphasize the potential of ChatGPT-4o in data cleaning, further research is needed to focus on the limitations of our study.