Explore the challenges and insights of evaluating Large Language Models (LLMs) in this 36-minute podcast episode featuring Aniket Kumar Singh, CTO at MyEvaluationPal and ML Engineer at Ultium Cells. Delve into the importance of LLM evaluation, performance measurement techniques, and common obstacles faced in the field. Gain valuable insights on prompt engineering and model selection based on Aniket's research. Discover real-world applications of LLMs in healthcare, economics, and education, and learn about future directions for improving these powerful AI models. The discussion covers topics such as systems-level perspectives, model capabilities, AI confidence trends, agent architectures, and the balance between robust pipelines and prompts.
Evaluating the Effectiveness of Large Language Models - Challenges and Insights
Overview
Syllabus
[] Aniket's preferred coffee
[] Takeaways
[] Aniket's job and hobby
[] Evaluating LLMs: Systems-Level Perspective
[] Rule-based system
[] Evaluation Focus: Model Capabilities
[] LLM Confidence
[] Problems with LLM Ratings
[] Understanding AI Confidence Trends
[] Aniket's papers
[] Testing AI Awareness
[] Agent Architectures Overview
[] Leveraging LLMs for tasks
[] Closed systems in Decision-Making
[] Navigating model Agnosticism
[] Robust Pipeline vs Robust Prompt
[] Wrap up
Taught by
MLOps.community
