Methods of Fine-Tuning of Artificial Intelligence

Abstract

Approaches to fine-tuning of artificial intelligence based on two proposed methods are considered. For this purpose, a review of the main methods of retraining artificial intelligence was carried out, their advantages and disadvantages were determined. The proposed methods have their own characteristics, each of them is suitable for different types of tasks.

The "Step-by-Step" method is based on multiplying the information entered into the AI verif дication model and consolidating this information at each stage of training. Using this approach, it becomes possible to detect errors at the early stages and provide additional information to the model, which allows the artificial intelligence to learn the material better.

In its turn, the "All at once" method simultaneously introduces a large amount of information to the AI model at the initial stage, after which the verification of the fixed information begins by placing questions. This approach can be effective for tasks that require rapid learning and need complex comprehension of large amounts of information.

Each of the methods has its advantages and disadvantages, the effectiveness of each of them may vary depending on the specific context of application. The Step-by-Step method allows the AI to learn the details better, but may require more time and resources. The "All at once" method allows to achieve results faster, but it can lead to a superficial understanding of the material and increase the number of errors.

In this work, a schematic block of each of the two methods was developed, a comparative analysis of the effectiveness of these methods was carried out using the example of fine-tuning the ChatGPT model. An experimental approbation of the AI training process was carried out based on each of the methods, due to which a comparative assessment of the effectiveness of the results of fine-tuning was performed and appropriate conclusions were drawn. The results of the study can be useful for developers and researchers working in the field of artificial intelligence and can help to use a better method of retraining for specific tasks.

Therefore, modified methods of fine-tuning of artificial intelligence have been studied, which allow using a smaller amount of resources and obtaining high accuracy and efficiency of work for specific tasks.