Large language models (LLMs) serve as the foundation of numerous AI applications today. However, despite their remarkable proficiency in generating coherent text, questions linger regarding their ability in performing fine-grained linguistic annotation tasks, such as detecting nouns or verbs, or identifying more complex syntactic structures like clauses or T-units in input texts. These tasks require precise syntactic and semantic understanding of input text, and when LLMs underperform on specific linguistic structures, it raises concerns about their reliability for detailed linguistic analysis and whether their (even correct) outputs truly reflect an understanding of the inputs. In this paper, we empirically study recent LLMs performance across fine-grained linguistic annotation tasks. Through a series of experiments, we find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs. We show that the most capable LLM (Llama3-70b) makes notable errors in detecting linguistic structures, such as misidentifying embedded clauses, failing to recognize verb phrases, and confusing complex nominals with clauses. Our study provides valuable insights to inform future endeavors in LLM design and development.

Linguistic Blind Spots of Large Language Models

CMCL invites papers on cognitive modeling, cognitively-inspired natural language processing, and, more broadly, the alignment of language models with human cognition/perception. **[Read more...](https://cmclorg.github.io/)**

W04: Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2025)

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Hadi Amiri

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Linguistic Blind Spots of Large Language Models

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