Optimize Text Degree Distributions for Concept Learning

Optimize the degree distribution of text nodes in the concept–text bipartite graph—reflecting architectural design choices—to maximize the expected number of concepts learned under a fixed compute budget.

Background

In the proposed framework, text-to-concept connections are governed by degree distributions, which influence the efficacy of iterative peeling in learning concepts. The authors note that these distributions relate to model architecture and suggest optimization could improve concept learning.

They explicitly present this as part of the open questions, indicating the potential for architecture-aware degree distribution design analogous to code design in communication theory to enhance learning outcomes.