Today, LLMs are Swiss knives and MT one of their tools. Is this the end of MT research? In this talk, I argue that the connection between LLM and MT research is two-way. I present some of our recent work advancing multilingual LLMs, tools to estimate their quality, and how the two can be combined for test-time scaling. First, I present xCOMET, an open-source learned metric which integrates sentence-level evaluation and error span detection, exhibiting state-of-the-art performance across all types of meta-evaluation (sentence-level, system-level, and error span detection). Moreover, it does so while highlighting and categorizing error spans, thus enriching the quality assessment. Then, I present Tower, a suite of open multilingual LLMs for translation-related tasks. Tower models are created through continued pretraining on a carefully curated multilingual mixture of monolingual and parallel data. The combination of Tower with COMET reranking obtained the best results in 8 out of 11 language pairs in the WMT General Translation shared task, according to human evaluation. Finally, I describe EuroLLM, an ongoing EU-made project whose goal is to train an open multilingual LLM from scratch using the European HPC infrastructure (EuroHPC). The last release (EuroLLM-9B) supports 35 languages, including all 24 official EU languages, and it achieves strong results in various benchmarks, comparable or better than the best existing models of similar size.

xCOMET: https://huggingface.co/collections/Unbabel/xcomet-659eca973b3be2ae4ac023bb

Tower: https://huggingface.co/collections/Unbabel/tower-659eaedfe36e6dd29eb1805c

EuroLLM: https://huggingface.co/blog/eurollm-team/eurollm-9b

André F. T. Martins (PhD 2012, Carnegie Mellon University and Instituto Superior Técnico; https://andre-martins.github.io/) is an Associate Professor at Instituto Superior Técnico, University of Lisbon, researcher at Instituto de Telecomunicações, and the VP of AI Research at Unbabel. His research, funded by a ERC Starting Grant (DeepSPIN) and Consolidator Grant (DECOLLAGE), among other grants, include machine translation, quality estimation, structure and interpretability in deep learning systems for NLP. His work has received several paper awards at ACL conferences. He co-founded and co-organizes the Lisbon Machine Learning School (LxMLS), and he is a Fellow of the ELLIS society and co-director of the ELLIS Program in Natural Language Processing. He is a member of the R&I advisory group of EuroHPC, the European infrastructure for supercomputing.

Speech is our most natural way of communication and the loss of the ability to speak is therefore devastating to patients. A speech neuroprostheses that directly reconstructs speech processes from neural activity could provide a new means of communications to these severely affected patients. In this presentation, I will present some approaches to reconstruct different representations of speech from intracranial recordings and highlight how they can be used to build a speech neuroprosthesis. The decoding of speech processes is particularly challenging, as not only the neural, but also the target signal has complex, nonlinear dynamics. I will stress the use of interpretable machine learning models for this task to ensure that meaningful activity is decoded and scientific insights might be generated as a side product.

Dr. Christian Herff is an assistant professor in the School for Mental Health and Neuroscience at Maastricht University where he leads the invasive BCI research line. His research interest lays in the application of machine learning technology to neurophysiological data for Brain-Computer Interfaces and neuroscience research. With a particular focus on the decoding of speech processes from intracranial data, he tries to improve the lives of severely paralyzed patients while simultaneously improving our understanding of complex higher order cognition. He emphasizes the ability to achieve interpretable results based on computational models. In particular, visualization of complex dynamic models, such as deep neural networks, is of interest to him.

Multilingual language models (MLMs), such as XLM-R or BLOOM, are pretrained on data covering many languages and share their parameters across all languages. This modeling approach has several powerful advantages, such as allowing similar languages to exert positive influence on each other, and enabling cross-lingual task transfer (i.e., fine-tuning on some source language(s), then using the model on different target languages). The success of such transfer, however, depends on the model's ability to effectively share information between different languages in its parameter space. Yet, the cross-lingual information sharing mechanisms within MLMs are still not fully understood. In this talk, I will present our recent research that investigates this question from three different perspectives: encoding of typological relationships between languages within MLMs, language-wise modularity of MLMs and the influence of training examples in specific languages on predictions made in others.

Ekaterina Shutova is an Associate Professor at the ILLC, University of Amsterdam, where she leads the Amsterdam Natural Language Understanding Lab and the Natural Language Processing & Digital Humanities research unit. She received her PhD from the University of Cambridge, and then worked as a research scientist at the University of California, Berkeley. Ekaterina’s current research focuses on few-shot learning for language interpretation tasks, multilingual NLP, generalisability and robustness of NLP models and interpretability in deep learning. Her prominent service roles include Program Chair of ACL 2025, Senior Action Editor of ACL Rolling Review, Action Editor of Computational Linguistics and Demonstrations chair at EMNLP 2022. She is also an ELLIS scholar.