Balancing Faithfulness and Utility
Patrick discusses the challenges of augmented generation, particularly the tension between producing fluent responses and maintaining faithfulness to retrieved information. He highlights the difficulty in determining whether a question is answerable based on the context of the retrieved data, raising questions about the trade-offs between providing potentially inaccurate answers and abstaining from responses altogether. The conversation delves into the complexities of achieving a balance in model performance, emphasizing the nuanced decisions that developers face.In this clip
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Machine Learning Street Talk (MLST)
Patrick Lewis (Cohere) - Retrieval Augmented Generation
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