Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. These synthetic query-document pairs can then be used to train a retriever. However, InPars and, more recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to generate such datasets. In this work we introduce InPars-v2, a dataset generator that uses open-source LLMs and existing powerful rerankers to select synthetic query-document pairs for training. A simple BM25 retrieval pipeline followed by a monoT5 reranker finetuned on InPars-v2 data achieves new state-of-the-art results on the BEIR benchmark. To allow researchers to further improve our method, we open source the code, synthetic data, and finetuned models: https://github.com/zetaalphavector/inPars/tree/master/tpu
@article{arxiv.2301.01820,
title = {InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval},
author = {Vitor Jeronymo and Luiz Bonifacio and Hugo Abonizio and Marzieh Fadaee and Roberto Lotufo and Jakub Zavrel and Rodrigo Nogueira},
journal= {arXiv preprint arXiv:2301.01820},
year = {2023}
}