What Language Models Really Know About Language: Benchmarks That Challenge Industry Assumptions

This article cuts through industry assumptions by providing rigorous empirical evidence on how language models actually understand syntactic and semantic structures. For graduate students and NLP researchers, systematic evaluation through benchmarks like Holmes, TWT, and SENSE prompting is essential—it reveals that LLM linguistic competence cannot be taken for granted but must be tested for each specific task. Understanding which models and strategies work for particular linguistic applications directly shapes the design of more effective AI systems and determines whether LLMs can be reliably used as research tools in computational linguistics.