MLG 034 Large Language Models 1

This episode explores large language models (LLMs), building upon previous discussions on natural language processing and transformers, emphasizing the importance of understanding transformers for grasping the core of LLMs. The discussion covers scaling laws, which dictate the scaling of architecture, data sets, and training, along with emergent abilities like question answering. Architectural evolutions, such as the Mixture of Experts (MOE), are examined for their role in enhancing scale and efficiency, and the episode also delves into training, tuning, and alignment techniques, including supervised fine-tuning and reinforcement learning from human feedback. Scaling laws research indicates that test loss decreases as a power law function of model size, data size, and training compute, leading to the development of models like GPT-3 with 175 billion parameters. Emergent abilities, such as in-context learning and multi-step reasoning, arise sharply in larger-scale models, though their existence as fundamental properties is debated. The introduction of Mixture of Experts (MOE) allows for greater scale and efficiency by activating only specific experts relevant to the input, improving computational performance.