The chapter explores the concept of combining multiple embedding models through ensemble learning to improve query document distance and ranking in private search. It discusses the importance of using uncorrelated models with diverse characteristics and the role of scalers in weighing each embedding. The conversation also delves into the evaluation process using human assessors and the challenges of delivering synthesized search results through summarization.

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