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VISLA Benchmark: Evaluating Embedding Sensitivity to Semantic and Lexical Alterations
This paper introduces the VISLA (Variance and Invariance to Semantic and Lexical Alterations) benchmark, designed to evaluate the semantic and lexical understanding of language models.
Test-Time Training for Speech
In this paper, we study the application of Test-Time Training (TTT) as a solution to handling distribution shifts in speech applications.
Training Diffusion Classifiers with Denoising Assistance
Score-matching and diffusion models have emerged as state-of-the-art generative models for both conditional and unconditional generation.
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