Abstract
The capacity of ChatGPT, a cutting-edge conversational AI model, to produce text responses that resemble those of a human being in a range of conversational contexts has attracted a lot of attention. By using extensive pretraining on a variety of textual data, ChatGPT generates discourse with impressive fluency and coherence, opening up new possibilities for improving human-computer interaction. In this study, we give a thorough analysis of ChatGPT's capabilities and constraints, examining how well it generates interesting and natural-sounding discussions. We explore several facets of ChatGPT's dialogue production through empirical evaluation and qualitative analysis, such as its flexibility across domains, responsiveness to input cues, and propensity to produce a range of contextually relevant and varied responses. Additionally, we look at methods for adjusting ChatGPT to certain conversational tasks and evaluate how different training regimens affect its Index Terms Data Collection and Preprocessing, Machine Learning Models based, Prediction and Visualization, Real-time Update.
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