27 citations · 64 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams
Desnes Nunes, Ricardo Primi, Ramon Pires +2
The present study aims to explore the capabilities of Language Models (LMs) in tackling high-stakes multiple-choice tests, represented here by the Exame Nacional do Ensino Médio (E…
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…
ExaRanker: Explanation-Augmented Neural Ranker
Fernando Ferraretto, Thiago Laitz, Roberto Lotufo +1
Recent work has shown that inducing a large language model (LLM) to generate explanations prior to outputting an answer is an effective strategy to improve performance on a wide ra…
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…