Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation

TL;DR

This paper introduces JSA-RAG, a novel end-to-end training method for retrieval-augmented generation models that improves over traditional approaches by reducing bias and variance in gradient estimates.

Contribution

It develops a joint stochastic approximation algorithm for training RAG models, addressing bias and high variance issues in previous methods.

Findings

JSA-RAG significantly outperforms vanilla RAG and VRAG on five datasets.

The method improves generation quality, retrieval accuracy, and gradient stability.

Extensive experiments validate the effectiveness of JSA-RAG across tasks.

Abstract

Retrieval-augmented generation (RAG) has become a widely recognized paradigm to combine parametric memory with non-parametric memories. An RAG model consists of two serial connecting components (retriever and generator). A major challenge in end-to-end optimization of the RAG model is that marginalization over relevant passages (modeled as discrete latent variables) from a knowledge base is required. Traditional top-K marginalization and variational RAG (VRAG) suffer from biased or high-variance gradient estimates. In this paper, we propose and develop joint stochastic approximation (JSA) based end-to-end training of RAG, which is referred to as JSA-RAG. The JSA algorithm is a stochastic extension of the EM (expectation-maximization) algorithm and is particularly powerful in estimating discrete latent variable models. Extensive experiments are conducted on five datasets for two tasks (open-domain question answering, knowledge-grounded dialogs) and show that JSA-RAG significantly outperforms both vanilla RAG and VRAG. Further analysis shows the efficacy of JSA-RAG from the perspectives of generation, retrieval, and low-variance gradient estimate.