Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents. Combining large language models (LLMs) with embedding-based retrieval models, recent work shows promising results on the zero-shot retrieval problem, i.e., no access to labeled data from the target domain. Two such popular paradigms are generation-augmented retrieval or GAR (generate additional context for the query and then retrieve), and retrieval-augmented generation or RAG (retrieve relevant documents as context and then generate answers). The success of these paradigms hinges on (i) high-recall retrieval models, which are difficult to obtain in the zero-shot setting, and (ii) high-precision (re-)ranking models which typically need a good initialization. In this work, we propose a novel GAR-meets-RAG recurrence formulation that overcomes the challenges of existing paradigms. Our method iteratively improves retrieval (via GAR) and rewrite (via RAG) stages in the zero-shot setting. A key design principle is that the rewrite-retrieval stages improve the recall of the system and a final re-ranking stage improves the precision. We conduct extensive experiments on zero-shot passage retrieval benchmarks, BEIR and TREC-DL. Our method establishes a new state-of-the-art in the BEIR benchmark, outperforming previous best results in Recall@100 and nDCG@10 metrics on 6 out of 8 datasets, with up to 17% relative gains over the previous best.

翻译：给定一个查询和文档语料库，信息检索任务的目标是输出相关文档的排序列表。结合大语言模型与基于嵌入的检索模型，近期研究在零样本检索问题（即无法访问目标域的标注数据）上取得了显著成果。两种主流范式分别为生成增强检索（通过为查询生成额外上下文后进行检索）和检索增强生成（检索相关文档作为上下文后生成答案）。这些范式的成功依赖于：（i）高召回率的检索模型——这在零样本场景下难以实现；（ii）高精度的重排序模型——通常需要良好的初始化。本文提出一种新型的GAR与RAG循环融合框架，克服了现有范式的挑战。该方法在零样本场景下通过GAR（检索阶段）和RAG（重写阶段）的迭代优化实现性能提升。核心设计原则是：重写-检索阶段提升系统的召回率，最终的重排序阶段提升精确率。我们在零样本段落检索基准BEIR和TREC-DL上进行了广泛实验。在BEIR基准中，我们的方法在8个数据集中的6个上实现了Recall@100和nDCG@10指标的最优结果，相较于此前最佳方法最高带来17%的相对增益，确立了新的领先水平。