Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance

Felix Brei; Johannes Frey; Lars-Peter Meyer

Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance

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arxiv 2405.17076 v1 pith:K5VB24CG submitted 2024-05-27 cs.AI cs.CLcs.IR

Felix Brei , Johannes Frey , Lars-Peter Meyer This is my paper

classification cs.AIcs.CLcs.IR

keywords languageassistancemodelstrainingacademicaffordablebillioncommodity

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In this work we will show that language models with less than one billion parameters can be used to translate natural language to SPARQL queries after fine-tuning. Using three different datasets ranging from academic to real world, we identify prerequisites that the training data must fulfill in order for the training to be successful. The goal is to empower users of semantic web technology to use AI assistance with affordable commodity hardware, making them more resilient against external factors.

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SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection
cs.IR 2025-07 conditional novelty 6.0 of 10

A controlled prompting protocol shows open-weight LLMs rely heavily on memorized Wikidata entities when writing SPARQL queries, with accuracy collapsing on a rarely used benchmark.

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