66 citations · 140 across the 23 of their papers we have counts for
4 papers · 1 filter
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…
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…
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo +4
Recent work has shown that small distilled language models are strong competitors to models that are orders of magnitude larger and slower in a wide range of information retrieval…