The article presents the results of a study of the capabilities of the large language model (GPT-4) to identify the intentional structure of the text. In the course of the research, the following tasks were solved: to develop a methodology for classifying therapists intentions in psychotherapeutic discourse; to create optimal instructions for working with the model; to analyze the impact of completeness of instructions on the accuracy of identifying intentions by the model; to study the relationship between the accuracy of the classification of intentions by the model with the frequency of occurrence of intentions in the text and the consistency of expert assessments. To analyze the intentional structure of texts, an original Methodology for classifying therapists intentions in psychotherapeutic discourse has been developed. Using this technique, a team of 3 experts who make decisions about the presence of intent in a remark independently of each other conducted marking up of 14 sessions in Russian (a total of 692 replicas of the therapist). The task of identifying the therapists intentions was solved by the GPT-4o-mini and GPT-o1 models. The model revealed the intentions of the psychotherapist, realized by him in specific speech utterances (replicas). The conducted research demonstrated the significant capabilities of the large GPT-4 language model in solving the problem of identifying the speakers speech intentions. The achieved accuracy in classifying the therapists intentions turned out to be at the level of the best indicators obtained in works on a similar subject. It is shown that improving the instructions significantly increases the quality of the models operation, and the complexity of the tasks assigned to the model is related to the accuracy of forecasts. Different criteria for the presence of intentions in the therapists remarks significantly changed the accuracy of the models predictions.
Vanin et al. (2025) studied this question.