Visual assistants are becoming ubiquitous, yet their effectiveness varies drastically across languages and cultures. This talk presents an overview of the critical issue of multicultural disparity in image–text models. We'll explore this gap through three lenses: evaluation, training, and generation. First, I'll introduce benchmarks like MaRVL designed to quantify multilingual and multicultural competence. Next, we'll delve into techniques for mitigating these disparities in model training. Finally, we'll examine the emerging challenges and opportunities in multicultural visual generation.

Emanuele Bugliarello is a research scientist at Google DeepMind based in Grenoble, France where he works on improving evaluation and capabilities of multimodal generative models. He completed his PhD in the NLP Section at the University of Copenhagen, while spending time at DeepMind, Google, Mila and Spotify. Previously, he studied computer and communication sciences at EPFL, Tongji University and Politecnico di Torino.

Large language models (LLMs) are increasingly used in a variety of applications across the globe but do not provide equal utility across languages. In this talk, I will discuss multilingual evaluation of LLMs in two practical settings: conversational instruction-following and usage of quantized models. For the first part, I will focus on a specific aspect of multilingual conversational ability where errors result in a jarring user experience: generating text in the user’s desired language. I will describe a new benchmark and evaluation of a range of LLMs. We find that even the strongest models exhibit language confusion, i.e., they fail to consistently respond in the correct language. I will discuss what affects language confusion, how to mitigate it, and potential extensions. In the second part, I will discuss the first evaluation study of quantized multilingual LLMs across languages. We find that automatic metrics severely underestimate the negative impact of quantization and that human evaluation—which has been neglected by prior studies—is key to revealing harmful effects. Overall, I highlight limitations of multilingual LLMs and challenges of real-world multilingual evaluation.

Sebastian Ruder is a research scientist at Meta based in Berlin, Germany where he works on improving evaluation and benchmarking of large language models (LLMs). He previously led the Multilinguality team at Cohere with the objective to improve the multilingual capabilities of Cohere's LLMs. Before that he was a research scientist at Google DeepMind. He completed his PhD in Natural Language Processing (NLP) at the Insight Research Centre for Data Analytics, while working as a research scientist at Dublin-based text analytics startup AYLIEN. Previously, he studied Computational Linguistics at the University of Heidelberg, Germany and at Trinity College, Dublin.

The Mímir Project is an initiative by the Norwegian government that aims to assess the significance and influence of copyrighted materials in the development and performance of generative large language models (LLMs) tailored to the Norwegian languages. This collaborative effort involves three leading institutions from different regions of the country: the National Library of Norway (NB), the University of Oslo (UiO), and the Norwegian University of Science and Technology (NTNU); each contributing unique expertise in language technology, corpus curation, model training, copyright law, and computational linguistics. The ultimate goal of the project was to gather empirical evidence that informed the formulation of a compensation scheme for authors whose works are utilized by these advanced artificial intelligence (AI) systems, ensuring that intellectual property rights are respected and adequately compensated.

Javier de la Rosa is a Research Scientist at the Artificial Intelligence Lab at the National Library of Norway. A former Postdoctoral Fellow in Natural Language Processing at UNED, he holds a PhD in Hispanic Studies with a specialization in Digital Humanities by the University of Western Ontario, and a Masters in Artificial Intelligence by the University of Seville. Javier has previously worked as a Research Engineer at the Stanford University, and as the Technical Lead at the University of Western Ontario CulturePlex Lab. He is interested in Natural Language Processing applied to historical and literary text, with a special focus on large language models.