Infusing Syntax and Semantics into LLMs
Abstract
Abstract Despite impressive success, Large Language Models (LLMs) sometimes generate outputs with flawed linguistic structure. We analyze the effect of directly infusing syntactic and semantic information into LLMs. To demonstrate the value of our proposals, we focus on the translation of natural language queries to SQL — in particular dealing with languages with less resources than English, to better investigate how much help we can get from low-cost syntactic and semantic information. We show that linguistic analysis can significantly boost language models and even surpass existing best systems for some languages.
Fields of Study & Subjects
Natural Language Processing Techniques
Semantic Web and Ontologies
Mathematics
Computing
and Information Processing
Publication details
- Journal / source
- International Journal of Computational Intelligence Systems
- Volume
- 19
- Issue
- 1
- Publication date
- 17 June 2026
- Language
- EN