Mano Bharathi M

Adaptive Compute Efficient Learning via Conceptual-Criticality (Student Abstract)

The computational cost of large language models (LLMs) is a primary obstacle to sustainable deployment. Static resource allocation is inefficient, as not all inputs require the same depth of processing. We propose a framework for adaptive, compute-efficient learning via conceptual criticality, which