Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation
This work addresses a key bottleneck in optimizing retrieval-augmented generation models, which are widely used for combining parametric and non-parametric memories, by providing a more stable training method.
The paper tackled the challenge of biased or high-variance gradient estimates in end-to-end training of retrieval-augmented generation models by proposing joint stochastic approximation, which significantly outperformed vanilla RAG and VRAG on five datasets for tasks like open-domain question answering and knowledge-grounded dialogs.
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.