From RAG to ReST: A Survey of Advanced Techniques in Large Language Model Development

Large Language Models (LLMs) face challenges like temporal limitations, complex computations, and inaccuracies. Researchers are integrating LLMs with external data sources to address these issues. Transformer architecture, with self-attention mechanisms, has outperformed previous models. Various transformer-based models serve specific tasks. Techniques like RAG and PAL enhance LLMs' real-time information access and computational accuracy. Fine-tuning methods like LoRA and prompt tuning make LLMs more efficient. Reinforcement Learning techniques like RLHF and ReST are used for aligning models with human preferences. Scaling and fine-tuning strategies are discussed for improved model performance.