Exploring the capabilities and limitations of GPT and Chat GPT in natural language processing

Abstract

Natural Language Processing (NLP) has seen tremendous advancements with the development of Generative Pretrained Transformer (GPT) models and their conversational variant, ChatGPT. These language models have been shown to generate contextually appropriate and coherent responses to natural language prompts, making them highly useful for various NLP applications. However, there are still limitations to their performance and understanding these limitations is crucial for their effective utilization. This paper presents a comprehensive analysis of the capabilities and limitations of GPT and ChatGPT, covering their architecture, training processes, and evaluation metrics. The study also evaluates the performance of these models on various NLP tasks, including language translation, question-answering, and text summarization. The results reveal that while these models excel in certain tasks, they still face challenges in understanding context, generating diverse responses, and handling rare or out-of-domain inputs. The study concludes by discussing potential solutions and future research directions for improving the performance of GPT and ChatGPT in NLP applications.

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