Abstract We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to standard fine-tuning methods that optimize for downstream models, CA-SSLR integrates language and speaker embeddings from[…]

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Abstract With the rise and ubiquity of larger deep learning models, the need for high-quality compression techniques has been growing in order to deploy these models widely. The sheer parameter count of some models makes[…]

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Abstract Gradient-based sampling algorithms are a cornerstone of modern Bayesian computation, widely used in applications ranging from probabilistic programming to diffusion models.  While these methods perform exceptionally well in continuous domains, extending them to discrete[…]

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Abstract The current paradigm for safety alignment of large language models (LLMs) follows a one-size-fits-all approach: the model refuses to interact with any content deemed unsafe by the model provider. This approach lacks flexibility in[…]

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Abstract Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or[…]

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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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