The Use of Large Language Models for Marking Datasets in Natural Language Processing - Krystina Makoveichuk; Alexander Olifirov; Georgiy Demenchuk; Yan Makoveichuk (Современные информационные технологии и IT-образование)
The article offers a method of using large language models of OpenAI company for marking text data through an accessible software interface. The developed method is the first step of solving the problem from the category of processing natural language. The problem in general consists in classifying courses that may be multi-class or multi-labeled using machine learning algorithms. The loosely structured rate data provided included a large number of different columns and were 275,811 rows long, but categories, subcategories and items were not defined. Their markup was done using the large text-davinci-003 language model, using functions written in Python. The tagging results were normalized and analysed. To check the quality of the model, a sample of courses for each subject were manually marked in the source data. More than 98% of the courses were classified correctly, so this method of automated data marking with a large language model can be recommended for later use.