Re3val: reinforced and reranked² data-efficient generative retrieval

Abstract

Generative retrieval models encapsulate the understanding of queries within their parameters. However, to minimize uncertainty and attain a more comprehensive understanding, it is crucial to thoroughly explore the knowledge derived from decoding and auxiliary indexes. In this regard, we present Re3val, which facilitates a generative retrieval model to reinforce and rerank utilizing limited data. Specifically, we generate questions from our pre-training dataset to counterbalance epistemic uncertainty and bridge the domain discrepancy between the pre-training and fine-tuning datasets. Additionally, we utilize the REINFORCE to infuse information sourced from the undifferentiable constrained decoding algorithm. Furthermore, our page title reranker integrates contexts procured via Dense Passage Retrieval to rerank the retrieved page titles. Subsequently, we extract contexts from the KILT database using the reranked page titles and rerank them by relevance. Upon grounding the top five reranked contexts, Re3val demonstrates the Top 1 KILT scores compared to all other generative retrieval models across five KILT datasets.