ChatGPT and Language Models: A New Era in Linguistic Communication

Abstract

Annotation:

Research topic and purpose: This study is dedicated to the topic "ChatGPT and language models in a new era of linguistic communication" and aims to study how artificial intelligence-based language models (especially ChatGPT) affect linguistic communication. In particular, the study is aimed at assessing the effectiveness of interactive communication between humans and machines through the use of language models in modern communication tools.

Research problem: The following questions were considered as the main problem in the study: how high is the ability of language models to understand human language and create user-friendly responses? Can these models work effectively enough in terms of linguistic accuracy, pragmatic flexibility, and contextual requirements of communication?

Research methodology: The study used a combination of qualitative and quantitative methods. The mechanisms of operation of ChatGPT and other advanced language models, the process of linguistic analysis, text comprehension, and response generation were studied. User experience data was also analyzed and the models were evaluated by performing linguistic tests.

Main results: According to the results of the study, ChatGPT demonstrates a high level of flexibility and accuracy in human communication. It was found that it is capable of generating effective responses at the levels of linguistic pragmatics, syntax, and semantics. It was also shown that these models have great practical importance in the fields of language learning, education, and information exchange. At the same time, limitations such as contextual ambiguity and, in some cases, a lack of deep understanding of the topic were noted.

Conclusions and recommendations: The main conclusions of the study show that the contribution of language models to the development of linguistic communication is significant, but it is important to further improve them and increase contextual understanding. As recommendations, the use of richer datasets in training language models, increasing their pragmatic accuracy, and taking into account different cultural contexts of communication are proposed. This serves to increase the practical and theoretical effectiveness of linguistic communication tools based on artificial intelligence.