no code implementations • 13 Jan 2025 • Thales Sales Almeida, Giovana Kerche Bonás, João Guilherme Alves Santos, Hugo Abonizio, Rodrigo Nogueira
In a rapidly evolving knowledge landscape and the increasing adoption of large language models, a need has emerged to keep these models continuously updated with current events.
no code implementations • 15 Oct 2024 • Hugo Abonizio, Thales Sales Almeida, Thiago Laitz, Roseval Malaquias Junior, Giovana Kerche Bonás, Rodrigo Nogueira, Ramon Pires
This report presents Sabi\'a-3, our new flagship language model, and Sabiazinho-3, a more cost-effective sibling.
no code implementations • 14 Mar 2024 • Thales Sales Almeida, Hugo Abonizio, Rodrigo Nogueira, Ramon Pires
We introduce Sabi\'a-2, a family of large language models trained on Portuguese texts.
1 code implementation • 23 Nov 2023 • Ramon Pires, Thales Sales Almeida, Hugo Abonizio, Rodrigo Nogueira
Recent advancements in language models have showcased human-comparable performance in academic entrance exams.
1 code implementation • 10 Jul 2023 • Hugo Abonizio, Luiz Bonifacio, Vitor Jeronymo, Roberto Lotufo, Jakub Zavrel, Rodrigo Nogueira
Our toolkit not only reproduces the InPars method and partially reproduces Promptagator, but also provides a plug-and-play functionality allowing the use of different LLMs, exploring filtering methods and finetuning various reranker models on the generated data.
no code implementations • 16 Apr 2023 • Ramon Pires, Hugo Abonizio, Thales Sales Almeida, Rodrigo Nogueira
By evaluating on datasets originally conceived in the target language as well as translated ones, we study the contributions of language-specific pretraining in terms of 1) capturing linguistic nuances and structures inherent to the target language, and 2) enriching the model's knowledge about a domain or culture.
1 code implementation • 4 Jan 2023 • Vitor Jeronymo, Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, Roberto Lotufo, Jakub Zavrel, Rodrigo Nogueira
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 queries for documents.
1 code implementation • 12 Dec 2022 • Guilherme Rosa, Luiz Bonifacio, Vitor Jeronymo, Hugo Abonizio, Marzieh Fadaee, Roberto Lotufo, Rodrigo Nogueira
We find that the number of parameters and early query-document interactions of cross-encoders play a significant role in the generalization ability of retrieval models.
1 code implementation • COLING 2022 • Hugo Abonizio, Leandro Rodrigues de Souza, Roberto Lotufo, Rodrigo Nogueira
The zero-shot cross-lingual ability of models pretrained on multilingual and even monolingual corpora has spurred many hypotheses to explain this intriguing empirical result.
1 code implementation • 6 Jun 2022 • Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo, Hugo Abonizio, Marzieh Fadaee, Roberto Lotufo, Rodrigo Nogueira
This has made distilled and dense models, due to latency constraints, the go-to choice for deployment in real-world retrieval applications.
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1 code implementation • 30 May 2022 • Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo, Hugo Abonizio, Roberto Lotufo, Rodrigo Nogueira
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.
1 code implementation • 10 Feb 2022 • Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, Rodrigo Nogueira
In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks.