We present ACADATA, a high-quality parallel dataset for academic translation, that consists of two subsets: ACAD-TRAIN, which contains approximately 1.5 million author-generated paragraph pairs across 96 language directions and ACAD-BENCH, a curated evaluation set of almost 6,000 translations covering 12 directions. To validate its utility, we fine-tune two Large Language Models (LLMs) on ACAD-TRAIN and benchmark them on ACAD-BENCH against specialized machine-translation systems, general-purpose, open-weight LLMs, and several large-scale proprietary models. Experimental results demonstrate that fine-tuning on ACAD-TRAIN leads to improvements in academic translation quality by +6.1 and +12.4 d-BLEU points on average for 7B and 2B models respectively, while also improving long-context translation in a general domain by up to 24.9% when translating out of English. The fine-tuned top-performing model surpasses the best propietary and open-weight models on academic translation domain. By releasing ACAD-TRAIN, ACAD-BENCH and the fine-tuned models, we provide the community with a valuable resource to advance research in academic domain and long-context translation.

As Large Language Models (LLMs) become available in a wider range of domains and applications, evaluating the truthfulness of multilingual LLMs is an issue of increasing relevance. TruthfulQA (Lin et al., 2022) is one of few benchmarks designed to evaluate how models imitate widespread falsehoods. However, it is strongly English-centric and starting to become outdated. We present VeritasQA, a context- and time-independent truthfulness benchmark built with multilingual transferability in mind, and available in Spanish, Catalan, Galician and English. VeritasQA comprises a set of 353 questions and answers inspired by common misconceptions and falsehoods that are not tied to any particular country or recent event. We release VeritasQA under an open license and present the evaluation results of 15 models of various architectures and sizes.

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 IberoBench, a multilingual, multi-task benchmark for Iberian languages (i.e., Basque, Catalan, Galician, European Spanish and European Portuguese) built on the LM Evaluation Harness framework. The benchmark consists of 62 tasks divided into 179 subtasks. We evaluate 33 existing LLMs on IberoBench on 0- and 5-shot settings. We also explore the issues we encounter when working with the Harness and our approach to solving them to ensure high-quality evaluation.

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. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Collecting voice resources for speech recognition systems is a multifaceted challenge, involving legal, technical, and diversity considerations. However, it is crucial to ensure fair access to voice-driven technology across diverse linguistic backgrounds. We describe an ongoing effort to create an extensive, high-quality, publicly available voice dataset for future development of speech technologies in Catalan through the Mozilla Common Voice crowd-sourcing platform. We detail the specific approaches used to address the challenges faced in recruiting contributors and managing the collection, validation, and recording of sentences. This detailed overview can serve as a source of guidance for similar initiatives across other projects and linguistic contexts. The success of this project is evident in the latest corpus release, version 16.1, where Catalan ranks as the most prominent language in the corpus, both in terms of recorded hours and when considering validated hours. This establishes Catalan as a language with significant speech resources for language technology development and significantly raises its international visibility.

Large language models have amply proven their great capabilities, both in downstream tasks and real-life settings. However, low- and mid-resource languages do not have access to the necessary means to train such models from scratch, and often have to rely on multilingual models despite being underrepresented in the training data. For the particular case of the Catalan language, we prove that continued pre-training with vocabulary adaptation is a better alternative to take the most out of already pre-trained models, even if these have not seen any Catalan data during their pre-training phase. We curate a 26B tokens corpus and use it to further pre-train BLOOM, giving rise to the FLOR models. We perform an extensive evaluation to assess the effectiveness of our method, obtaining consistent gains across Catalan and Spanish tasks. The models, training data, and evaluation framework are made freely available under permissive licenses.

Current LLM-based applications are becoming steadily available for everyone with a reliable access to technology and the internet. These applications offer benefits to their users that leave those without access to them at a serious disadvantage. Given the vastly large amount of data needed to train LLMs, the gap between languages with access to such quantity of data and those without it is currently larger than ever. Aimed at saving this gap, the Aina Project was created to provide Catalan with the necessary resources to keep being relevant in the context of AI/NLP applications based on LLMs. We thus present a set of strategies to consider when improving technology support for a mid- or low-resource language, specially addressing sustainability of high-quality data acquisition and the challenges involved in the process. We also introduce a large amount of new annotated data for Catalan. Our hope is that those interested in replicating this work for another language can learn from what worked for us, the challenges that we faced, and the sometimes disheartening truth of working with mid- and low-resource languages.

We present and describe two language resources in this paper: CATalog 1.0, the largest text corpus in Catalan to date, and CURATE (Corpus Utility for RAting TExt), a modular, parallelizable pipeline used for processing and scoring documents based on text quality that we have optimised to run in High Performance Cluster (HPC) environments. In the coming sections we describe our data preprocessing pipeline at length; traditional pipelines usually implement a set of binary filters such that a given document is either in or out. In our experience with Catalan, in lower-resource settings it is more practical to instead assign a document a soft score to allow for more flexible decision-making. We describe how the document score is calculated and highlight its interpretability by showing that it is significantly correlated with human judgements as obtained from a comparative judgement experiment. We additionally describe the different subcorpora that make up CATalog 1.0.

Marc Pàmies, Joan Llop, Francesco Multari, Nicolau Duran-Silva, César Parra-Rojas, Aitor Gonzalez-Agirre, Francesco Alessandro Massucci, Marta Villegas. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

The research reported in this paper is part of the activities carried out within the CLARIN (Common Language Resources and Technology Infrastructure) project, a large-scale pan-European project to create, coordinate and make Language Resources and Technologies (LRT) available and readily useable. CLARIN is devoted to the creation of a persistent and stable infrastructure serving the needs of the European Humanities and Social Sciences (HSS) research community. HSS researchers will be able to efficiently access distributed resources and apply analysis and exploitation tools relevant for their research. Hereby we present a real use case addressed as a CLARIN scenario and the implementation of a demonstrator that enables us to foresee the potential problems and contributes to the planning of the implementation phase. It deals with how to support researchers interested in harvesting and analyzing data from historical press archives. Therefore, we address the integration and interoperability of distributed and heterogeneous research data and analysis tools.

The ISLE project is a continuation of the long standing EAGLES initiative, carried out under the Human Language Technology (HLT) programme in collaboration between American and European groups in the framework of the EU-US International Research Co-operation, supported by NSF and EC. In this paper we concentrate on the current position of the ISLE Computational Lexicon Working Group (CLWG), whose activities aim at defining a general schema for a multilingual lexical entry (MILE), as the basis for a standard framework for multilingual computational lexicons. The needs and features of existing Machine Translation systems provide the main reference points for the process of consensual definition of the MILE. The overall structure of the MILE will be illustrated with particular attention to some of the issues raised for multilingual lexicons by the need of expressing complex transfer conditions among translation equivalents