提出新训练方法，让检索增强生成模型更准更快。
Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation
- 用联合随机逼近算法优化检索与生成的端到端训练
- 在5个数据集上显著优于传统方法，生成质量更高
- 适合想提升RAG模型性能的研究者和工程师
检索增强生成（RAG）是一种结合参数化记忆与非参数化记忆的主流范式，由检索器和生成器串联构成。端到端优化的主要挑战在于需对知识库中的相关文本段落（建模为离散隐变量）进行边缘化处理。传统的Top-K边缘化和变分RAG（VRAG）存在梯度估计偏差或方差过高的问题。本文提出并开发了基于联合随机逼近（JSA）的端到端RAG训练方法，称为JSA-RAG。JSA是EM算法的随机扩展，特别适用于离散隐变量模型的估计。在五个数据集上针对开放域问答和知识驱动对话两个任务进行大量实验，结果表明JSA-RAG显著优于原始RAG和VRAG。进一步分析从生成、检索和低方差梯度估计三个角度验证了其有效性。
原文摘要 · Abstract (English)
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.
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