Evaluating AI Systems
The conversation explores the complexities of evaluating large language models (LLMs) and the surrounding tools that influence their performance. It highlights the challenges of black box evaluations, particularly concerning training data contamination, and the need for transparency in understanding model behavior. Insights into the importance of separating tool evaluations from overall system assessments reveal potential gaps in current methodologies.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - 671
Related Questions
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business in the episode MLOps for GenAI Applications // Harcharan Kabbay // #256?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?