Papers by Iker García-Ferrero
MedMT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain (2024.lrec-main)
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Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello
| Challenge: | Existing studies on large language models for medical applications have focused on a single language . medical mT5 outperforms both encoders and similar sized text-to-text models in English, French, and Italian benchmarks . |
| Approach: | They propose to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. |
| Outcome: | The proposed model outperforms encoders and similar sized models on the Spanish, French, and Italian benchmarks while being competitive with current state-of-the-art models in English. |
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark (2023.findings-emnlp)
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| Challenge: | Existing methods for evaluating large language models using annotated benchmarks are in trouble . data contamination can cause wrong scientific conclusions being published . |
| Approach: | They argue that the evaluation of NLP tasks using annotated benchmarks is in trouble . they define different levels of data contamination and propose a community effort . |
| Outcome: | The proposed measures should detect when data from a benchmark was exposed to a model and flag papers with conclusions compromised by data contamination. |
This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have grammatical knowledge but fail to interpret negation . a recent study shows that LLMs struggle with negative sentences . |
| Approach: | They propose to use a dataset to grasp LLMs' generalization and inference capability . they also fine-tuned models to assess whether the understanding of negation can be trained . |
| Outcome: | The proposed model is able to generalize and infer negation in 400,000 sentences . but it is suboptimal when it comes to negation, a key step in natural language processing . |
T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks (2023.findings-emnlp)
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| Challenge: | Annotation projection is a task of transporting labels from source to target language without high-quality training data. |
| Approach: | They propose an annotation projection approach that leverages pretrained text2text models and machine translation technology to generate annotated data. |
| Outcome: | The proposed approach outperforms existing methods on intrinsic and extrinsic tasks in 5 Indo-European and 8 low-resource African languages. |
Benchmarking Meta-embeddings: What Works and What Does Not (2021.findings-emnlp)
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| Challenge: | Existing methods to build meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. |
| Approach: | They propose a unified framework for a fair and objective meta-embedding evaluation using intrinsic and extrinsic tasks. |
| Outcome: | The proposed framework outperforms existing methods on intrinsic and extrinsic evaluation benchmarks and outperformed existing methods. |
GUIDEX: Guided Synthetic Data Generation for Zero-Shot Information Extraction (2025.findings-acl)
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| Challenge: | Existing domain-specific IE systems require expert schema design, data annotation, and model training. |
| Approach: | They propose a method that automatically defines domain-specific schemas and infers guidelines and generates synthetically labeled instances. |
| Outcome: | The proposed method improves on seven zeroshot Named Entity Recognition benchmarks. |
Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings (2022.findings-emnlp)
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| Challenge: | Existing studies have proposed data-based cross-lingual transfer as an effective technique for cross-linguistic sequence labelling, but they have failed to perform well. |
| Approach: | They propose to use data-based cross-lingual transfer to train supervised models from a source language to unlabelled target languages. |
| Outcome: | The proposed techniques outperform data-based cross-lingual transfer approaches in a zero-shot setting. |
Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque (2025.emnlp-main)
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Oscar Sainz, Naiara Perez, Julen Etxaniz, Joseba Fernandez de Landa, Itziar Aldabe, Iker García-Ferrero, Aimar Zabala, Ekhi Azurmendi, German Rigau, Eneko Agirre, Mikel Artetxe, Aitor Soroa
| Challenge: | Instructing language models with user intent requires large instruction datasets limited to a limited set of languages. |
| Approach: | They propose to use existing LLMs and synthetically generated instructions to train models with user intent. |
| Outcome: | The proposed model outperforms base non-instructed models on Basque without Basque instructions. |