20 citations · 34 across the 9 of their papers we have counts for
14 papers
Translate, then Detect: Leveraging Machine Translation for Cross-Lingual Toxicity Classification
Samuel J. Bell, Eduardo Sánchez, David Dale +3
Multilingual toxicity detection remains a significant challenge due to the scarcity of training data and resources for many languages. While prior work has leveraged the translate-…
Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque
Oscar Sainz, Naiara Perez, Julen Etxaniz +9
Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to…
Emergent Abilities of Large Language Models under Continued Pretraining for Language Adaptation
Ahmed Elhady, Eneko Agirre, Mikel Artetxe
Continued pretraining (CPT) is a popular approach to adapt existing large language models (LLMs) to new languages. When doing so, it is common practice to include a portion of Engl…
WiCkeD: A Simple Method to Make Multiple Choice Benchmarks More Challenging
Ahmed Elhady, Eneko Agirre, Mikel Artetxe
We introduce WiCkeD, a simple method to increase the complexity of existing multiple-choice benchmarks by randomly replacing a choice with "None of the above", a method often used…
BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation
The Omnilingual MT Team, Pierre Andrews, Mikel Artetxe +14
BOUQuET is a multi-way, multicentric and multi-register/domain dataset and benchmark, and a broader collaborative initiative. This dataset is handcrafted in 8 non-English languages…
Linguini: A benchmark for language-agnostic linguistic reasoning
Eduardo Sánchez, Belen Alastruey, Christophe Ropers +3
We propose a new benchmark to measure a language model's linguistic reasoning skills without relying on pre-existing language-specific knowledge. The test covers 894 questions grou…