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Found 3 papers, 3 papers with code
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs.
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
Large language and vision-language models are rapidly being deployed in practice thanks to their impressive capabilities in instruction following, in-context learning, and so on.
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward.
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