Papers by Irene Baucells
Building a Data Infrastructure for a Mid-Resource Language: The Case of Catalan (2024.lrec-main)
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Aitor Gonzalez-Agirre, Montserrat Marimon, Carlos Rodriguez-Penagos, Javier Aula-Blasco, Irene Baucells, Carme Armentano-Oller, Jorge Palomar-Giner, Baybars Kulebi, Marta Villegas
| Challenge: | Aina Project aims to provide Catalan with the resources needed to keep its relevance in AI/NLP applications. |
| Approach: | They propose a set of strategies to consider when improving technology support for a mid- or low-resource language . they propose annotated datasets and a framework to make models ready to use . |
| Outcome: | The Aina Project aims to provide Catalan with the necessary resources to keep its relevance in AI/NLP-related industry and research. |
Dynamic Stance: Modeling Discussions by Labeling the Interactions (2023.findings-emnlp)
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| Challenge: | Stance detection is a popular task that has been modeled as a static task, but its limitations are strong topic-dependent. |
| Approach: | They propose to model stance as a dynamic task by focusing on interactions between a message and their replies. |
| Outcome: | The proposed model shows portability across topics and languages. |
FLOR: On the Effectiveness of Language Adaptation (2024.lrec-main)
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Severino Da Dalt, Joan Llop, Irene Baucells, Marc Pamies, Yishi Xu, Aitor Gonzalez-Agirre, Marta Villegas
| Challenge: | Large language models have amply proven their capabilities, but low- and mid-resource languages do not have access to the necessary means to train such models from scratch. |
| Approach: | They use a 26B tokens corpus to further pre-train BLOOM, giving rise to FLOR models. |
| Outcome: | The proposed model achieves consistent gains across Catalan and Spanish tasks. |
Multi-LMentry: Can Multilingual LLMs Solve Elementary Tasks Across Languages? (2025.emnlp-main)
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Luca Moroni, Javier Aula-Blasco, Simone Conia, Irene Baucells, Naiara Perez, Silvia Paniagua Suárez, Anna Sallés, Malte Ostendorff, Júlia Falcão, Guijin Son, Aitor Gonzalez-Agirre, Roberto Navigli, Marta Villegas
| Challenge: | a recent study focused on complex, high-level tasks, but LMentry is limited to English . a multilingual evaluation of large language models is needed to address this gap, authors say . |
| Approach: | They propose a compact benchmark that enables systematic evaluation of large language models . they propose to use tasks that are trivial for humans but remain surprisingly difficult for LLMs . |
| Outcome: | The proposed benchmark is limited to English, leaving its insights linguistically narrow. |
IberoBench: A Benchmark for LLM Evaluation in Iberian Languages (2025.coling-main)
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Irene Baucells, Javier Aula-Blasco, Iria de-Dios-Flores, Silvia Paniagua Suárez, Naiara Perez, Anna Salles, Susana Sotelo Docio, Júlia Falcão, Jose Javier Saiz, Robiert Sepulveda Torres, Jeremy Barnes, Pablo Gamallo, Aitor Gonzalez-Agirre, German Rigau, Marta Villegas
| Challenge: | Existing multi-task benchmarks for Large Language Models are limited to English . a new benchmark is needed to evaluate models on a range of tasks . |
| Approach: | They propose a multilingual, multi-task benchmark for Iberian languages built on the LM Evaluation Harness framework. |
| Outcome: | The proposed benchmark covers 62 tasks divided into 179 subtasks and is available in Iberian, Basque, Catalan, Galician, European Spanish and European Portuguese. |