  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.
