Abstract The rapid advancement of large language models (LLMs) has unlocked a myriad of possibilities for positive societal impact, ranging from enhancing accessibility and communication to supporting disaster response and public health initiatives. However, the[…]

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Abstract Self-consistency (SC) has been demonstrated to enhance the performance of large language models (LLMs) across various tasks and domains involving short content. However, does this evidence support its effectiveness for long-context problems? In this talk, we examine the[…]

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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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