Arguments for why finetuning has become less useful over time, as well as some opinions as to where the field is going with Emmanuel Ameisen.

This is a talk from Mastering LLMs: A survey course on applied topics for Large Language Models.

More resources are available here:
https://bit.ly/appliedllms

00:00: Introduction and Background
01:23: Disclaimers and Opinions
01:53: Main Themes: Trends, Performance, and Difficulty
02:53: Trends in Machine Learning
03:16: Evolution of Machine Learning Practices
06:03: The Rise of Large Language Models (LLMs)
08:18: Embedding Models and FineTuning
11:17: Benchmarking Prompts vs. FineTuning
12:23: FineTuning vs. RAG: A Comparative Analysis
25:03: Adding Knowledge to Models
33:14: Moving Targets: The Challenge of FineTuning
38:10: Essential ML Practices: Data and Engineering
44:43: Trends in Model Prices and Context Sizes
47:22: Future Prospects of FineTuning