Evaluating ChatGPT: Strengths and Limitations in NLP Problem Solving

Abstract

This paper critically analyzes ChatGPT’s problem-solving performance on a range of natural language processing (NLP) tasks. Using a comparative methodology, it compares ChatGPT’s performance with that of its predecessor, GPT-3.5, in seven different domains: summarization, named entity recognition, arithmetic, natural language inference, symbolic and logical reasoning, question answering, conversation, and arithmetic. The process entails a methodical assessment highlighting ChatGPT’s replies’ quantitative and qualitative elements. The findings show that although ChatGPT performs very well on math and question-answering tasks, it struggles with summarization and commonsense reasoning. The conversation sheds light on the subtleties of these findings while considering the applications and development of AI. The article concludes that although ChatGPT is a significant progress in natural language processing, its uneven problem-solving performance highlights the need for continued development and optimization of artificial intelligence models. This study aids in understanding the state and promise of AI-driven language models in challenging problem-solving situations.

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