Evaluating Model Performance
Patrick discusses the challenges of measuring the perceived utility of information in machine learning models, highlighting the gap between model quality and available datasets for evaluation. He emphasizes the need for more sophisticated metrics, as current benchmarks rely on outdated methods that fail to accurately assess correctness. The conversation also touches on the importance of developing better evaluation tools to improve the reliability of results in the field.In this clip
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Machine Learning Street Talk (MLST)
Patrick Lewis (Cohere) - Retrieval Augmented Generation
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