161 citations · 205 across the 17 of their papers we have counts for
17 papers · 1 filter
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,…
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…
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…
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…
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…
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis
Mikel Zubillaga, Oscar Sainz, Ainara Estarrona +2
Cross-lingual transfer-learning is widely used in Event Extraction for low-resource languages and involves a Multilingual Language Model that is trained in a source language and ap…
Latxa: An Open Language Model and Evaluation Suite for Basque
Julen Etxaniz, Oscar Sainz, Naiara Perez +6
We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters. Latxa is based on Llama 2, which we continue pretraining on a new Basque c…
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
Oscar Sainz, Jon Ander Campos, Iker García-Ferrero +3
In this position paper, we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble. The worst kind of data contamina…
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
Oscar Sainz, Iker García-Ferrero, Rodrigo Agerri +3
Large Language Models (LLMs) combined with instruction tuning have made significant progress when generalizing to unseen tasks. However, they have been less successful in Informati…
IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases
Iker García-Ferrero, Jon Ander Campos, Oscar Sainz +2
Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like…
What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories
Oscar Sainz, Oier Lopez de Lacalle, Eneko Agirre +1
Language Models are the core for almost any Natural Language Processing system nowadays. One of their particularities is their contextualized representations, a game changer featur…
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle +2
Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as Textual Entailment tasks using verbalizations, with strong performance in zero-shot and few…
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle +2
The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples. In this d…
Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Bonan Min, Hayley Ross, Elior Sulem +6
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses…
Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka +2
Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simp…
Ask2Transformers: Zero-Shot Domain labelling with Pre-trained Language Models
Oscar Sainz, German Rigau
In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, t…