Evaluating LLMs: Introduction

- Welcome to evaluating large language models. I'm Sinan Ozdemir, a data scientist, entrepreneur, author, and lecturer with over a decade of experience in AI, NLP, and machine learning. This video series is designed to equip you with the knowledge and skills to assess LLM performance effectively ensuring that these powerful and often unwieldy AI tools meet your real world needs. We'll start with the foundations of LLM evaluation, exploring why it even matters, and the core metrics that underpin evaluation. From there, we'll delve into how to evaluate generative and understanding based tasks. Ensuring that models perform well across a wide range of applications. We'll then turn our attention to how to use benchmarks effectively, leveraging data sets like MMLU, truthful QA, MTEB, and more to assess LLM capabilities in a structured manner. As we progress, we'll start to tackle more advanced techniques like probing LLMs for their internal world representations and evaluating our fine tuning efforts for task-specific applications in a resource constrained environment. Finally, we'll dive into a multitude of case studies, exploring real-world evaluations strategies and the challenges of keeping models reliable in production systems with examples like agentic work, retrieval augmented generation, and time series regression. By the end of the series, you should have a comprehensive understanding of LLM evaluation from foundational metrics to much more advanced methods. So, whether you're a developer, researcher, or just an enthusiast, this series will prepare you to evaluate and optimize LLMs for cutting-edge AI applications. So, let's get started.