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How Low Can We Go? Quantization Effects on LLM SPARQL Generation
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DOI：10.1007/978-3-031-99554-5_18.png)
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Abstract
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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:
SPARQL
Large Language Models
Quantization
Knowledge Graphs
Resource Efficiency
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