InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval

• URL: http://arxiv.org/abs/2301.01820v4
• Date: Fri, 26 May 2023 22:16:38 GMT
• Title: InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
• Authors: Vitor Jeronymo, Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, Roberto Lotufo, Jakub Zavrel, Rodrigo Nogueira
• Abstract summary: We introduce InPars-v2, a dataset generator that uses open-source LLMs and 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.
• Score: 4.888022358881737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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