Abstract Recent years saw the rise of multilingual language models that achieve high levels of performance for a large number of tasks, with some of them handling hundreds of languages at once. Premodern languages are[…]

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Abstract In the age of data- and capital-driven machine learning, the gap between technological advancements for high- and low-resource language varieties keeps growing, leaving many with the greatest need for language technologies without access to[…]

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Abstract SoloAudio is an innovative diffusion-based generative model designed for target sound extraction (TSE). It trains latent diffusion models on audio, replacing the previous U-Net backbone with a skip-connected Transformer that operates on latent features.[…]

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Abstract Just as instructions have enabled complex user tasks and zero-shot optimization via prompts, so can instructions in retrieval enable complex information needs with zero-shot adaptation. In this talk, we describe efforts to build a[…]

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Abstract Language model pretraining has been a remarkably strong recipe for cross-task and cross-domain generalization in NLP. However, these gains have come at the expense of control: we rarely control the training data for language[…]

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Abstract Information dissemination for current events has traditionally consisted of professionally collected and produced materials, leading to large collections of well-written news articles and high-quality videos. As a result, most prior work in event analysis[…]

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Abstract Effective models of human attitudes and behavior can empower applications ranging from immersive environments to social policy simulation. However, traditional simulations have struggled to capture the complexity and contingency of human behavior. I argue[…]

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