Systematic exploration and in-depth analysis of ChatGPT architectures progression

Abstract

The fast evolution of artificial intelligence frameworks has resulted in the creation of increasingly sophisticated large language models (LLM), ChatGPT being the most famous one. This study paper dives into this LLM with a case study of ChatGPT’s architecture and provides a thorough comparative analysis of its numerous versions, tracking its history from its conception to its most recent incarnations. This research intends to give a full knowledge of the model’s history by investigating the underlying mechanisms and enhancements provided in each edition. The comparative analysis covers key aspects such as model size, training data, fine-tuning techniques, and performance metrics. Furthermore, this study evaluates the limits of ChatGPT in its many incarnations. These limitations include common sense reasoning difficulties, biased replies, verbosity, sensitivity to input wording, and others. Each constraint is investigated for potential remedies and workarounds. This research article also provides a complete analysis of the ChatGPT architecture and its progress through multiple iterations. It gives vital insights for academics, developers, and users wanting to harness the promise of ChatGPT while managing its restrictions by exploring both the model’s strengths and limitations. The distinctiveness of this paper rests in its comprehensive assessment of ChatGPT’s architectural development and its practical strategy for resolving the myriad difficulties in producing cohesive and contextually relevant replies.

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