Related Experiment Video
Updated: Jul 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
LLMs as a platform for studying constraint interaction: Motivation and challenges
Ethan Gotlieb Wilcox1, Elissa L Newport2
1Department of Linguistics, Georgetown University, USA ethan.wilcox@georgetown.edu.
None:
Large Language Models (LLMs) can serve as tools for understanding how probabilistic constraints interact during language acquisition. To motivate such use cases of LLMs, we discuss several examples from allied fields, including neurobiology and animal behavior, of how soft constraints shape learning and development in cognitive systems. We end by outlining four challenges that LLM cognitive modeling should address in the coming decade.
Related Concept Videos
Lagrange Multipliers: Two Constraints
Lagrange Multipliers: One Constraint
Constraints and Statical Determinacy
Locus of Control
Lagrange Multipliers: Problem Solving
Purposive Learning