Evaluating LLMs
The discussion dives into the challenges of evaluating large language models, emphasizing the importance of trust and effective assessment methods. Participants share their experiences with surveys that reveal the community's struggles and insights, highlighting the disconnect between benchmark claims and real-world applications. Open-sourcing the data fosters collaboration and understanding, paving the way for improved evaluation practices in AI.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
787: MLOps: The Job and The Key Tools — with Demetrios Brinkmann
Related Questions
Tell me about Demetrios Brinkmann in the episode Evaluation Panel // Large Language Models in Production Conference Part II and the clip Dynamic Panel Introduction
What can we learn from user testing based on the episode Understanding User Research: Surveys, Myths, and the Role of AI with Els Aerts and the clip Survey Insights?