Evaluating LLMs Effectively
Exploring the nuances of large language model evaluation, Patrick highlights the potential pitfalls of model bias and the benefits of using ensemble methods to enhance performance. By aggregating smaller models, not only can one achieve cost-efficiency, but also mitigate the tendency of models to favor their own outputs. Additionally, insights from human evaluators can be leveraged to improve correlations in evaluation metrics, paving the way for more reliable assessments.In this clip
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Machine Learning Street Talk (MLST)
Patrick Lewis (Cohere) - Retrieval Augmented Generation
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