Machine Translation
Marathon 2026

As speech translation systems and speech-enabled large language models (LLMs) continue to scale, efficiency is emerging as a central challenge rather than a secondary concern. But what does it actually mean for a speech model to be efficient? In this talk, we discuss the many axes along which efficiency can be measured and which metrics are used, highlighting what their implications and limits are. We then explore practical approaches to improving efficiency in speech models and speech LLMs (but not only), spanning from architectural choices, to inference strategies, and post-training solutions, with considerations regarding their effectiveness and performance trade-offs. In conclusion, we highlight open research questions and potential directions for future work on the topic.

Marco Gaido is a researcher at Fondazione Bruno Kessler (Trento, Italy), specializing in speech translation. He obtained his PhD in Information and Communication Technology from the University of Trento in 2023, graduating cum laude with a thesis on direct speech translation systems. His research focuses on improving the quality, efficiency, and explainability of SpeechLLM, with 60+ peer-reviewed publications including top-tier venues such as ACL, EMNLP, ICLR, and TACL. His work has received multiple awards, including the ACL Outstanding Paper & SAC Award, COLING Outstanding Paper Award, and the Anthony C. Clarke Award for the 2023 EAMT Best Thesis, and he is a member of ELLIS. In addition to his research contributions, he has experience in large-scale systems and open-source development, having contributed to frameworks such as Apache Spark, NeMo, and fairseq, and previously worked in big data engineering roles.

The race to scale LLMs has long been obsessed with quantity, but the real frontier is engineering data quality. Despite the "model collapse" narrative surrounding AI-generated content, we'll show how to systematically curate and synthesize high-quality pre-training data at scale. Using our 628-billion-word German corpus [1] as a case study, we'll walk through the mechanics of a modern pre-training data pipeline — from heuristic filters to synthetic generation — and show the measurable gains these choices deliver when training from scratch. This is a no-nonsense look at what actually makes data work for LLMs today.

Letitia Parcalabescu holds a PhD in Computational Linguistics and a background in Physics and Computer Science. She is an AI researcher at Aleph Alpha Research, focusing on training interpretable reasoning models and curating and synthesizing data for large-scale pretraining. Letitia also runs the YouTube channel AI Coffee Break with Letitia, where she explains cutting-edge AI research papers and tech.

The rise of Large Language Models (LLMs) has deeply influenced the field of machine translation. Given their ability for instruction following and multilingual capabilities, translation has become "one more task" for many developers. But translation remains a very nuanced problem. In this talk we will see how years of research on machine translation, and very specifically on machine translation evaluation, influence the development of multilingual LLMs. We will put special emphasis on incorporating quality metrics in all stages of the development process, from pre-training down to reinforcement learning. At the same time, we will address how LLMs are influencing the development of evaluation metrics, while at the same time pointing out their inherent limitations.

For more than 20 years, David Vilar has been actively involved in machine translation research. In this time, he has participated in the evolution from statistical phrase-based models to neural machine translation systems and LLM-based systems. He is currently a Staff Research Scientist at Google, where he works on the Google Translate and Gemini teams. His core research focuses heavily on leveraging evaluation metrics to drive modern generative models. In addition to his scientific publications, he has contributed to the community with the release of several open-source and open-weight models, including Jane, Sockeye, Gemma 3 and TranslateGemma.