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Found 3 papers, 1 papers with code
SelectIT: Selective Instruction Tuning for Large Language Models via Uncertainty-Aware Self-Reflection
In this work, we propose a novel approach, termed SelectIT, that capitalizes on the foundational capabilities of the LLM itself.
Emage: Non-Autoregressive Text-to-Image Generation
Compared with autoregressive baselines that needs to run one thousand times, our model only runs 16 times to generate images of competitive quality with an order of magnitude lower inference latency.
One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code
Moreover, our model supports self-supervised pretraining with the same sparsely activated way, resulting in better initialized parameters for different modalities.
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