Linguistic Competence

Linguistic competence in language models (LLMs) focuses on evaluating the models' understanding of linguistic structures and their ability to use language functionally, not just statistically. Current research uses benchmarks like Holmes to assess various aspects of this competence, including syntax, morphology, and semantics, often employing probing techniques on architectures such as BERT and GPT-like models to analyze internal representations. This research is crucial for improving LLMs' robustness and reliability, informing our understanding of both human and artificial language processing, and ultimately leading to more effective and ethically sound applications of these powerful technologies.