Syntactic Knowledge

Syntactic knowledge, the understanding of sentence structure and grammatical rules, is a central focus in computational linguistics, with research aiming to understand how this knowledge is acquired and represented in both humans and artificial systems. Current research investigates how large language models (LLMs), including transformer-based architectures and graph attention networks, implicitly and explicitly learn syntactic information, often leveraging techniques like probing classifiers and dependency tree analysis to evaluate their understanding. These advancements have implications for improving natural language processing tasks such as machine translation, grammatical error correction, and educational tools, ultimately contributing to a deeper understanding of human language acquisition and more robust AI systems.