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[…]
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[…]
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[…]
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[…]
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[…]
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[…]
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.[…]
