27 citations · 64 across the 11 of their papers we have counts for
6 papers · 1 filter
ExaRanker-Open: Synthetic Explanation for IR using Open-Source LLMs
Fernando Ferraretto, Thiago Laitz, Roberto Lotufo +1
ExaRanker recently introduced an approach to training information retrieval (IR) models, incorporating natural language explanations as additional labels. The method addresses the…
InRanker: Distilled Rankers for Zero-shot Information Retrieval
Thiago Laitz, Konstantinos Papakostas, Roberto Lotufo +1
Despite multi-billion parameter neural rankers being common components of state-of-the-art information retrieval pipelines, they are rarely used in production due to the enormous a…
InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval
Hugo Abonizio, Luiz Bonifacio, Vitor Jeronymo +3
Recent work has explored Large Language Models (LLMs) to overcome the lack of training data for Information Retrieval (IR) tasks. The generalization abilities of these models have…
NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval
Vitor Jeronymo, Roberto Lotufo, Rodrigo Nogueira
This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this s…
InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Vitor Jeronymo, Luiz Bonifacio, Hugo Abonizio +4
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 q…
In Defense of Cross-Encoders for Zero-Shot Retrieval
Guilherme Rosa, Luiz Bonifacio, Vitor Jeronymo +4
Bi-encoders and cross-encoders are widely used in many state-of-the-art retrieval pipelines. In this work we study the generalization ability of these two types of architectures on…