M. Fan

This work revisits joint multimodal representation learning and generation to produce linearly interpolatable embeddings that are directly consumable by generative decoders and ensures its representations function as both discriminative semantic descriptors and generative conditions.

Hard negatives generated in real-time by LLMs can significantly enhance the training of two-tower models, outperforming traditional sampling methods and reducing popularity bias in recommendations.