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.

通过引入 Stochastic RAG，本文提出了一种新颖的端到端优化方法，通过在 RAG 中将检索过程建模为一个无替换的随机抽样过程，利用可微的 Gumbel-top-k 提供了一个不同 iable 的无替换采样方法，从而实现了针对 RAG 的有效端到端优化，提高了六个数据集中六个任务的最先进结果。

随机 RAG：通过预期效用最大化的端到端检索增强生成

Stochastic RAG: End-to-End Retrieval-Augmented Generation through  Expected Utility Maximization

The well-known Gumbel-Max trick for sampling from a categorical distribution
can be extended to sample $k$ elements without replacement. We show how to
implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over
sequences, allowing to draw exact samples without replacement using a
Stochastic Beam Search. Even for exponentially large domains, the number of
model evaluations grows only linear in $k$ and the maximum sampled sequence
length. The algorithm creates a theoretical connection between sampling and
(deterministic) beam search and can be used as a principled intermediate
alternative. In a translation task, the proposed method compares favourably
against alternatives to obtain diverse yet good quality translations. We show
that sequences sampled without replacement can be used to construct
low-variance estimators for expected sentence-level BLEU score and model
entropy.

应用 Gumbel-Top-k 技巧和分解可重复采样，使用随机束搜索进行无重复抽样序列模型的研究，发现序列采样中存在随机束搜索和确定性束搜索之间的理论联系，这一方法在翻译任务中表现优异，且采样无重复序列可用于构造期望 BLEU 得分和模型熵的低方差估计。