> first, we fine-tune a LLaMA-3-8B powered LLaVA-1.5 and then employ it to recaption ∼1.3 billion images from the DataComp-1B dataset It's a bit surprising thi

> first, we fine-tune a LLaMA-3-8B powered LLaVA-1.5 and then employ it to recaption ∼1.3 billion images from the DataComp-1B dataset It's a bit surprising this works as well as they report. After all, you would think if you trained LLaVA on a dataset with low quality labels, you'd get a model that could only generate low quality labels, and re-labelling the dataset with the model would give you labels no better than the original low quality ones.