CaLID claims state-of-the-art 3D cardiac volume reconstruction from sparse 2D MRI slices via latent-space diffusion interpolation with a 24x speedup and no auxiliary inputs, but verification is impossible because the supplied manuscript body is a different paper.
LLMGuard: Guarding Against Unsafe LLM Behavior
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a tool that monitors user interactions with an LLM application and flags content against specific behaviours or conversation topics. To do this robustly, LLMGuard employs an ensemble of detectors.
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Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
CaLID claims state-of-the-art 3D cardiac volume reconstruction from sparse 2D MRI slices via latent-space diffusion interpolation with a 24x speedup and no auxiliary inputs, but verification is impossible because the supplied manuscript body is a different paper.