Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization
<|reference_start|>Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization: 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 datasets on a wide range of tasks, from open-domain question answering to fact verification to slot-filling for relation extraction and to dialogue systems. By applying this optimization method to a recent and effective RAG model, we advance state-of-the-art results on six out of seven datasets.<|reference_end|>
@article{zamani2024stochastic,
    title={Stochastic RAG: End-to-End Retrieval-Augmented Generation through
  Expected Utility Maximization},
    author={Hamed Zamani and Michael Bendersky},
    journal={arXiv preprint arXiv:2405.02816},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02816},
    primaryClass={cs.CL cs.IR cs.LG}
}