Eugene Lee

LLM-derived priors can dramatically enhance recommendation systems in cold-start scenarios, revealing significant demographic response variations.

Hierarchical cross-attention between vision encoders and LLMs unlocks superior vision-language alignment, outperforming flattened feature fusion methods on a range of benchmarks.