Gemma Boleda

LLMs use a surprisingly small, early-layer core for initial prediction, suggesting they may be more computationally efficient than their size implies.

Human-like color categories can emerge in neural agents by simply upsampling rare color terms and training with multiple listeners, closing the gap between artificial and human color lexicons.