11 papers
Cross-lingual Self-Consistency for Multilingual Reasoning with Language Models
Ahmed Elhady, Eneko Agirre, Mikel Artetxe
Despite expanding their multilingual coverage, the advanced reasoning capabilities of LLMs remain largely confined to a few high-resource languages like English. To address this, w…
Do not be greedy, Think Twice: Sampling and Selection for Document-level Information Extraction
Mikel Zubillaga, Oscar Sainz, Oier Lopez de Lacalle +1
Document-level Information Extraction (DocIE) aims to produce an output template with the entities, relations, and events of interest occurring in the given document. Standard prac…
Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference
Imanol Miranda, Ander Salaberria, Eneko Agirre +1
Dual-encoder Vision-Language Models (VLMs) such as CLIP are often characterized as bag-of-words systems due to their poor performance on compositional benchmarks. We argue that thi…
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 GarcÃa-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…