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🏢 Mila, Université De Montréal

Efficient Adversarial Training in LLMs with Continuous Attacks
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Large Language Models 🏢 Mila, Université De Montréal
Boosting LLM robustness against attacks efficiently: Continuous adversarial training in embedding space outperforms discrete methods, achieving improved robustness with less computation.
Amortizing intractable inference in diffusion models for vision, language, and control
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AI Generated Machine Learning Reinforcement Learning 🏢 Mila, Université De Montréal
Amortized sampling from complex posteriors using diffusion models is achieved via a novel data-free learning objective, Relative Trajectory Balance (RTB). RTB’s asymptotic correctness is proven, offe…