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Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization
May 7, 2024, 4:43 a.m. | Hamed Zamani, Michael Bendersky
cs.LG updates on arXiv.org arxiv.org
Abstract: This paper introduces Stochastic RAG--a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of marginalization and document independence, made in most prior work. Stochastic RAG casts the retrieval process in RAG as a stochastic sampling without replacement process. Through this formulation, we employ straight-through Gumbel-top-k that provides a differentiable approximation for sampling without replacement and enables effective end-to-end optimization for RAG. We conduct extensive experiments on seven diverse …
abstract arxiv assumptions cs.cl cs.ir cs.lg document novel optimization paper prior process rag replacement retrieval retrieval-augmented sampling simplifying stochastic through type utility work
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