GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval

• URL: http://arxiv.org/abs/2310.20158v1
• Date: Tue, 31 Oct 2023 03:52:08 GMT
• Title: GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval
• Authors: Daman Arora, Anush Kini, Sayak Ray Chowdhury, Nagarajan Natarajan, Gaurav Sinha, Amit Sharma
• Abstract summary: We propose a novel GAR-meets-RAG recurrence formulation that overcomes the challenges of existing paradigms. 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. 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.
• Score: 16.369071865207808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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