most citedQuati: A Brazilian Portuguese Information Retrieval Dataset from Native Speakers

1 citations · 2 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2026

Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines

Hugo Abonizio, Filipe Rocha Lopes, Roberto Lotufo +1

Brazil's Unified Health System (SUS) relies on official clinical guidelines that define diagnostic criteria, treatments, dosages, and monitoring procedures for over 200 million cit…

cs.CL2024

MLissard: Multilingual Long and Simple Sequential Reasoning Benchmarks

Mirelle Bueno, Roberto Lotufo, Rodrigo Nogueira

Language models are now capable of solving tasks that require dealing with long sequences consisting of hundreds of thousands of tokens. However, they often fail on tasks that requ…

cs.CL2024

ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language

Marcos Piau, Roberto Lotufo, Rodrigo Nogueira

Despite advancements in Natural Language Processing (NLP) and the growing availability of pretrained models, the English language remains the primary focus of model development. Co…

cs.IR2024★ 1 cited

Quati: A Brazilian Portuguese Information Retrieval Dataset from Native Speakers

Mirelle Bueno, Eduardo Seiti de Oliveira, Rodrigo Nogueira +2

Despite Portuguese being one of the most spoken languages in the world, there is a lack of high-quality information retrieval datasets in that language. We present Quati, a dataset…

cs.CL2024★ 1 cited

INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges

Jayr Pereira, Andre Assumpcao, Julio Trecenti +7

This paper introduces INACIA (Instrução Assistida com Inteligência Artificial), a groundbreaking system designed to integrate Large Language Models (LLMs) into the operational fram…

cs.CL2024

Lissard: Long and Simple Sequential Reasoning Datasets

Mirelle Bueno, Roberto Lotufo, Rodrigo Nogueira

Language models are now capable of solving tasks that require dealing with long sequences consisting of hundreds of thousands of tokens. However, they often fail on tasks that requ…

cs.IR2024

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…

cs.IR2024

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…