17 citations · 34 across the 17 of their papers we have counts for
4 papers · 1 filter
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…
GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction
Neil De La Fuente, Oscar Sainz, Iker García-Ferrero +1
Information Extraction (IE) systems are traditionally domain-specific, requiring costly adaptation that involves expert schema design, data annotation, and model training. While La…
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…