Milad Moradi1,*, Ke Yan2, David Colwell2, Matthias Samwald3, Rhona Asgari1
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1681-1698, 2025, DOI:10.32604/cmc.2025.061263
- 17 February 2025
Abstract This critical review provides an in-depth analysis of Large Language Models (LLMs), encompassing their foundational principles, diverse applications, and advanced training methodologies. We critically examine the evolution from Recurrent Neural Networks (RNNs) to Transformer models, highlighting the significant advancements and innovations in LLM architectures. The review explores state-of-the-art techniques such as in-context learning and various fine-tuning approaches, with an emphasis on optimizing parameter efficiency. We also discuss methods for aligning LLMs with human preferences, including reinforcement learning frameworks and human feedback mechanisms. The emerging technique of retrieval-augmented generation, which integrates external knowledge into LLMs, is More >