Why Fine-Tuning is Dead: Insights from Emmanuel Ameisen

In this talk from the ‘Mastering LLMs’ course, Hamel Husain and Emmanuel Ameisen discuss the diminishing importance of fine-tuning in the context of large language models (LLMs). Emmanuel argues that fine-tuning has become less crucial due to the advancements in LLMs and the rise of alternative techniques like retrieval-augmented generation (RAG). He outlines three main themes: trends in machine learning, performance observations, and the difficulty of fine-tuning. Emmanuel highlights that machine learning practices have evolved from deep learning and fine-tuning to more efficient methods like prompting and RAG. He presents data showing that RAG often outperforms fine-tuning, especially with larger models. The discussion also covers the challenges of adding domain-specific knowledge to models and the moving target problem due to rapid advancements in LLMs. Emmanuel emphasizes the importance of focusing on data quality, engineering, and evaluation over fine-tuning. The session concludes with insights into future trends, including the decreasing cost of tokens and expanding context windows, which may further reduce the need for fine-tuning.