Advancing machine learning: A comprehensive review of foundation models and their applications

In recent years, machine learning (ML) has made tremendous progress in different fields such as natural language processing (NLP), computer vision, and multi-modal learning. This paper discusses the state of the art in ML, dealing with the issues of parameter-efficient fine-tuning, transfer learning, few- and zero-shot learning, multi-modal learning, reinforcement learning from human feedback (RLHF), knowledge distillation and quantization, and sparse training. The authors evaluated these techniques in a range of ways, identifying advantages and disadvantages of each of them. Speculations about developments of ML in the near future and about possible fields of their applications are offered at the end of the paper.