27 citations · 50 across the 10 of their papers we have counts for
10 papers
Compressing Language Models for Specialized Domains
Miles Williams, George Chrysostomou, Vitor Jeronymo +1
Language models (LMs) excel at tasks across diverse domains, yet require substantial computational resources during inference. Compression techniques such as pruning and quantizati…
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…
Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval
Jimmy Lin, David Alfonso-Hermelo, Vitor Jeronymo +8
The advent of multilingual language models has generated a resurgence of interest in cross-lingual information retrieval (CLIR), which is the task of searching documents in one lan…
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…
mRobust04: A Multilingual Version of the TREC Robust 2004 Benchmark
Vitor Jeronymo, Mauricio Nascimento, Roberto Lotufo +1
Robust 2004 is an information retrieval benchmark whose large number of judgments per query make it a reliable evaluation dataset. In this paper, we present mRobust04, a multilingu…
A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022
Vitor Jeronymo, Guilherme Rosa, Surya Kallumadi +2
In this work we describe our submission to the product ranking task of the Amazon KDD Cup 2022. We rely on a receipt that showed to be effective in previous competitions: we focus…
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…
Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task
Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo +3
Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In this work, we experiment…