Papers by Raúl Vázquez
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki (2024.eacl-demo)
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Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh, Michele Boggia, Ona De Gibert, Shaoxiong Ji, Niki Andreas Loppi, Alessandro Raganato, Raúl Vázquez, Jörg Tiedemann
| Challenge: | a growing trend towards modularization is limiting the size and information that can be handled in large language models. |
| Approach: | They propose a framework for training massively multilingual modular machine translation systems at scale. |
| Outcome: | The proposed framework is adapted to train multilingual models at scale on NVIDIA GPUs. |
A Closer Look at Parameter Contributions When Training Neural Language and Translation Models (2022.coling-1)
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| Challenge: | Neural models and Transformers have been used for almost every NLP task . however, the intrinsic dynamics of the training procedure have not been studied in depth for highly complex network architectures. |
| Approach: | They analyze the learning dynamics of neural language and translation models using Loss Change Allocation indicator . they use a standard Transformer architecture to train a model with three learning objectives . |
| Outcome: | The proposed model is based on a standard model that is used for training tasks. |
Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care (2026.acl-long)
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Michal Štefánik, Timothee Mickus, Marek Kadlčík, Bertram Højer, Michal Spiegel, Raúl Vázquez, Aman Sinha, Josef Kuchař, Philipp Mondorf, Pontus Stenetorp
| Challenge: | Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations. |
| Approach: | They show that large language models often converge to accurate input embedding for numbers, based on sinusoidal representations. |
| Outcome: | The proposed representations are strikingly systematic, and are interchangeable in a large swathe of experimental setups. |
An Empirical Investigation of Word Alignment Supervision for Zero-Shot Multilingual Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Recent work has highlighted several flaws of MNMT models in zero-shot scenarios where language labels are ignored and the wrong language is generated. |
| Approach: | They propose to combine explicit alignment to language labels with word alignment supervision to improve zero-shot translations. |
| Outcome: | The proposed model improves on three multilingual MT benchmarks. |
On the differences between BERT and MT encoder spaces and how to address them in translation tasks (2021.acl-srw)
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| Challenge: | Various studies show that pretrained language models cannot replace encoders in neural machine translation despite their success in other tasks. |
| Approach: | They propose a supervised transformation from one into the other to improve the applicability of BERT in neural machine translation. |
| Outcome: | The proposed transformations show that they cannot replace encoders in MT despite their success in other tasks. |
Scaling Low-Resource MT via Synthetic Data Generation with LLMs (2025.emnlp-main)
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Ona de Gibert, Joseph Attieh, Teemu Vahtola, Mikko Aulamo, Zihao Li, Raúl Vázquez, Tiancheng Hu, Jörg Tiedemann
| Challenge: | a recent study has shown that LLM-generated synthetic data can improve low-resource machine translation performance . traditional data augmentation techniques like back-translation preserve the human-written target and synthesize the other . |
| Approach: | They construct a document-level synthetic corpus from English Europarl and extend it via pivoting to 147 additional language pairs. |
| Outcome: | The proposed model can significantly improve low-resource machine translation performance even when noisy. |
Your Model is Overconfident, and Other Lies We Tell Ourselves (2025.acl-long)
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| Challenge: | Analyzing 29 models, we find that difficulty is not linear or monotonic. |
| Approach: | They examine the interplay and divergence among various metrics for assessing intrinsic difficulty, including annotator dissensus, training dynamics, and model confidence. |
| Outcome: | The proposed model is based on 29 models on three datasets and analyzed by a linguistics team. |
GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models (2025.emnlp-demos)
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Hengyu Luo, Zihao Li, Joseph Attieh, Sawal Devkota, Ona de Gibert, Xu Huang, Shaoxiong Ji, Peiqin Lin, Bhavani Sai Praneeth Varma Mantina, Ananda Sreenidhi, Raúl Vázquez, Mengjie Wang, Samea Yusofi, Fei Yuan, Jörg Tiedemann
| Challenge: | Existing evaluation frameworks focus on English and a handful of high-resource languages, thereby overlooking the realistic performance of large language models in multilingual and lower-resourced scenarios. |
| Approach: | They propose a unified and lightweight framework that integrates 27 benchmarks under a standard ISO 639-3 language identifier system to enable seamless incorporation of new benchmarks. |
| Outcome: | The proposed framework integrates 27 benchmarks under a standard ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks. |