In this web content, the author discusses large language models (LLMs) and their significance in the field of AI. The author explains that LLMs, specifically Transformer models, have the ability to understand and generate sequences of text. They highlight the generalization capabilities of LLMs, as they can learn to perform different tasks with minimal examples. The author also explores the predictable scaling behavior of LLMs and the research trends related to training larger models. The content highlights the breakthroughs in instructGPT, which improves LLMs’ ability to follow instructions. The author touches upon the challenges of steerability and alignment in LLMs and discusses the use of supervised fine-tuning and reinforcement learning via human feedback to address these challenges. The content concludes by emphasizing the potential of LLMs as reasoning agents and the ongoing research in prompt engineering and LLM-centric tech stacks.
https://willthompson.name/what-we-know-about-llms-primer