Search Results for author: António Farinhas

Found 9 papers, 6 papers with code

Aligning Neural Machine Translation Models: Human Feedback in Training and Inference

no code implementations15 Nov 2023 Miguel Moura Ramos, Patrick Fernandes, António Farinhas, André F. T. Martins

A core ingredient in RLHF's success in aligning and improving large language models (LLMs) is its reward model, trained using human feedback on model outputs.

Language Modelling Machine Translation +1

An Empirical Study of Translation Hypothesis Ensembling with Large Language Models

1 code implementation17 Oct 2023 António Farinhas, José G. C. de Souza, André F. T. Martins

Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output.

Machine Translation Translation

Non-Exchangeable Conformal Risk Control

1 code implementation2 Oct 2023 António Farinhas, Chrysoula Zerva, Dennis Ulmer, André F. T. Martins

Split conformal prediction has recently sparked great interest due to its ability to provide formally guaranteed uncertainty sets or intervals for predictions made by black-box neural models, ensuring a predefined probability of containing the actual ground truth.

Conformal Prediction Time Series

Quality-Aware Decoding for Neural Machine Translation

1 code implementation NAACL 2022 Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, André F. T. Martins

Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search.

Machine Translation NMT +1

Sparse Communication via Mixed Distributions

1 code implementation ICLR 2022 António Farinhas, Wilker Aziz, Vlad Niculae, André F. T. Martins

Neural networks and other machine learning models compute continuous representations, while humans communicate mostly through discrete symbols.

Sparse Continuous Distributions and Fenchel-Young Losses

1 code implementation4 Aug 2021 André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae

In contrast, for finite domains, recent work on sparse alternatives to softmax (e. g., sparsemax, $\alpha$-entmax, and fusedmax), has led to distributions with varying support.

Audio Classification Question Answering +1

Sparse and Continuous Attention Mechanisms

2 code implementations NeurIPS 2020 André F. T. Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro M. Q. Aguiar, Mário A. T. Figueiredo

Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e. g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation).

Machine Translation Question Answering +4

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