Dispersion loss counteracts embedding condensation in small language models
This article explores the geometric phenomenon of 'embedding condensation' in small language models, where token vectors collapse into narrow cones. The authors propose a 'dispersion loss' training objective to counteract this collapse and improve model performance.
Why it matters
Understanding the geometric limitations of smaller models allows for more efficient training techniques, potentially narrowing the performance gap between small and large language models.
What makes LLMs better than small LMs? Data? Parameters? Geometry might play a role!
The content is a technical research summary focused on machine learning architecture without political or social framing.
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