The Evolution of Large Language Model: Models, Applications and Challenges

Large Language Models (LLMs) have attracted a lot of attention due to their success in natural language processing tasks. This paper provides a thorough overview by examining the architecture, applications, problems, assessment techniques, and future directions of LLM. With the constantly growing body of literature, a succinct yet comprehensive overview of recent developments is essential. Following the development of NLP, it highlights the move from rule-based systems to sophisticated transformer structures like as BERT and GPT. Important LLMs for text creation, translation, and summarization are mentioned, including T5, BART, and BioGPT. LLM performance is evaluated using metrics including as accuracy, perplexity, BLEU score, and ROUGE score. Research is still being done because of issues with bias, overfitting, and real-time processing. Future directions include managing longer contexts, lowering bias, and increasing efficiency through methods like federated learning. Continuous learning and multimodal LLMs are promising fields, as well as interpretive AI. In conclusion, LLMs have transformed natural language processing (NLP) and brought up both technical and ethical issues about the future of AI.