3 citations · 8 across the 24 of their papers we have counts for
8 papers · 1 filter
Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation
Sweta Agrawal, José G. C. de Souza, Ricardo Rei +5
Alignment with human preferences is an important step in developing accurate and safe large language models. This is no exception in machine translation (MT), where better handling…
Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis
Hippolyte Gisserot-Boukhlef, Ricardo Rei, Emmanuel Malherbe +3
Neural metrics for machine translation (MT) evaluation have become increasingly prominent due to their superior correlation with human judgments compared to traditional lexical met…
EuroLLM: Multilingual Language Models for Europe
Pedro Henrique Martins, Patrick Fernandes, João Alves +12
The quality of open-weight LLMs has seen significant improvement, yet they remain predominantly focused on English. In this paper, we introduce the EuroLLM project, aimed at develo…
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?
Josef Jon
This thesis argues that the currently widely used Natural Language Processing algorithms possibly have various limitations related to the properties of the texts they handle and pr…
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
Marcos Treviso, Nuno M. Guerreiro, Sweta Agrawal +7
While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies. Understanding these e…
QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation
Gonçalo R. A. Faria, Sweta Agrawal, António Farinhas +3
An important challenge in machine translation (MT) is to generate high-quality and diverse translations. Prior work has shown that the estimated likelihood from the MT model correl…
Can Automatic Metrics Assess High-Quality Translations?
Sweta Agrawal, António Farinhas, Ricardo Rei +1
Automatic metrics for evaluating translation quality are typically validated by measuring how well they correlate with human assessments. However, correlation methods tend to captu…
Is Context Helpful for Chat Translation Evaluation?
Sweta Agrawal, Amin Farajian, Patrick Fernandes +2
Despite the recent success of automatic metrics for assessing translation quality, their application in evaluating the quality of machine-translated chats has been limited. Unlike…