The podcast provides a detailed overview of Large Language Models (LLMs), exploring their fundamental concepts and evolution.
It discusses the shift in Natural Language Processing (NLP) towards a pre-training and fine-tuning paradigm, highlighting key architectures like encoder-only, decoder-only, and encoder-decoder models.
The podcast explains core processes such as pre-training, self-supervised learning, and fine-tuning, using examples like BERT. Furthermore, the sources describe how prompting and in-context learning are used to guide model behaviour without extensive retraining.
A significant focus is placed on the critical aspect of aligning LLMs with human intent through methods like supervised fine-tuning and Reinforcement Learning from Human Feedback (RLHF).
Finally, the podcast address the practical challenges and strategies involved in scaling LLM training and handling long sequences.
