Naiara Perez — accepted AI-conference papers

2025

IberoBench: A Benchmark for LLM Evaluation in Iberian Languages

Irene Baucells, Javier Aula-Blasco, Iria de-Dios-Flores, Silvia Paniagua Suárez, Naiara Perez, Anna Salles +9

COLING 2025main

The current best practice to measure the performance of base Large Language Models is to establish a multi-task benchmark that covers a range of capabilities of interest. Currently, however, such benchmarks are only available in a few high-resource languages. To address this situation, we present Ib…

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2025

Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque

Oscar Sainz, Naiara Perez, Julen Etxaniz, Joseba Fernandez de Landa, Itziar Aldabe, Iker Garc{\'i}a-Ferrero +6

EMNLP 2025

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 conventional instruction adaptation pipelines in low-resource scenarios. We assume a realistic scenario for low-resourc

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2025

Multi-LMentry: Can Multilingual LLMs Solve Elementary Tasks Across Languages?

Luca Moroni, Javier Aula-Blasco, Simone Conia, Irene Baucells, Naiara Perez, Silvia Paniagua Su{\'a}rez +7

EMNLP 2025

As large language models (LLMs) continue to improve, their evaluation increasingly centers on complex, high-level tasks, often at the expense of systematically assessing fundamental capabilities. To address this gap, recent work proposed LMentry, a compact benchmark comprising tasks that are trivial

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