3 papers
cs.CL2026
Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights
Eneko Valero, Maria Ribalta i Albado, Oscar Sainz +2
Large Language Models (LLMs) remain heavily centered on English, with limited performance in low-resource languages. Existing adaptation approaches, such as continual pre-training,…
cs.CL2026
SemBench: A Universal Semantic Framework for LLM Evaluation
Mikel Zubillaga, Naiara Perez, Oscar Sainz +1
Recent progress in Natural Language Processing (NLP) has been driven by the emergence of Large Language Models (LLMs), which exhibit remarkable generative and reasoning capabilitie…
cs.CL2026
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…