Algorithmic Issues, Challenges, and Theoretical Concerns of ChatGPT

Abstract

Large language model (LLM) ChatGPT has made tremendous progress in natural language processing (NLP), especially in human-quality text production, multilingual translation, and content creation. However, because of its extensive use, algorithmic issues, challenges, and theoretical queries come up. Fairness, bias, explainability, generalization, originality, inventiveness, and safety are the main topics of this study's examination of ChatGPT's intricate theoretical and algorithmic components. It looks into the possibility of explainability, transferability, generalization, bias in the data, and the model's capacity to provide original and imaginative content. It also covers possible issues including harmful use, disseminating incorrect information, and offensive or misleading content. These limitations can be addressed so that ChatGPT can be improved to provide LLMs that are more dependable, accountable, and long-lasting while posing no needless risks.

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