Next webinar Registration
Summary: Every once in a while, a new language model with gazillion parameters makes a big splash in Twitter, smashing the previous SOTA in some benchmarks or showing some impressive emerging capabilities. While some may argue that scaling will eventually solve NLP, others are skeptical about the scientific value of this trend. In this talk, I will argue that scaling is not just engineering, but also comes with exciting research questions. I will present some of our recent work in the topic, and discuss our efforts to make large language models more accessible for the community.
Bio:Mikel Artetxe is a Research Scientist at FAIR (Meta AI). His primary area of research is multilingual NLP. Mikel was one the pioneers of unsupervised machine translation, and has done extensive work on cross-lingual representation learning. More recently, he has also been working on natural language generation, few-shot learning, and large-scale language models. Prior to joining FAIR, Mikel did his PhD at the IXA group at the University of the Basque Country, and interned at DeepMind, FAIR and Google.
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