activity
20222025
most citedInPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval

27 citations · 50 across the 10 of their papers we have counts for

collaborators
Showing 2022Show all

5 papers · 1 filter

cs.IR2022★ 6 cited

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…

cs.CL2022★ 1 cited

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…

cs.IR2022★ 1 cited

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…

cs.IR2022★ 6 cited

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…

cs.CL2022★ 4 cited

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…