ORCID
- P. J. Wall: 0000-0002-5859-4425
Abstract
SPARQL is a powerful but complex language for querying knowledge graphs, motivating research into natural language-to-SPARQL generation using large language models (LLMs). While large, proprietary LLMs excel at this task, their resource requirements can limit practical deployment. This paper evaluates smaller, open-source LLMs (0.5B–9B parameters) with quantization methods (8-bit and 4-bit compression) to balance computational efficiency and query generation performance. Our findings demonstrate that 8-bit quantization can maintain or enhance performance in smaller models, whereas 4-bit quantization leads to notable degradation, especially for larger models. This highlights the potential of quantized, smaller LLMs for SPARQL generation in resource-constrained scenarios and provides insights for optimizing specialized NLP tasks.
Keywords
Knowledge Graphs, Large Language Models, Quantization, Resource Efficiency, SPARQL
DOI Link
Publication Date
2026-01-01
Event
Satellite events held at the 22nd European Semantic Web Conference, ESWC 2025
Publication Title
The Semantic Web: ESWC 2025 Satellite Events, Proceedings
Publisher
Springer Science and Business Media Deutschland GmbH
ISBN
9783031995538
ISSN
0302-9743
First Page
99
Last Page
102
Deposit Date
2026-01-21
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Murtagh-White, Matt; Wall, P. J.; and O’Sullivan, Declan, "How Low Can We Go? Quantization Effects on LLM SPARQL Generation" (2026). Research Outputs: 2025-Present. 9.
https://arrow.tudublin.ie/buschrsmro/9