🚨🚨 Excited to share our latest paper, now on @arxiv!
🖼️ We studied how unified VLMs, trained to generate both text and images (e.g., @metaai's Chameleon), exchange information between modalities, comparing them to standard VLMs.
Deep dive:👇
I just landed in Vancouver to present @NeurIPSConf the findings of our new work!
Few-shot learning and fine-tuning change the hidden layers of LLMs in a dramatically different way, even when they perform equally well on multiple-choice question-answering tasks.
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✨ Meet #ResiDual, a novel perspective on the alignment of multimodal latent spaces!
Think of it as a spectral "panning for gold" along the residual stream. It improves text-image alignment by simply amplifying task-related directions! 🌌🔍
arxiv.org/abs/2411.00246
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