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

2024/05/27 by Felix Brei, Brei, Felix, Johannes Frey +3 · 5 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Natural Language Processing Techniques #Text Readability and Simplification #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2405.17076

openalex publication_date 2024/05/27 · openalex created_date 2024/05/29 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
• SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection
• The benefits of query-based KGQA systems for complex and temporal questions in LLM era
• Conversational Lexicography: Querying Lexicographic Data on Knowledge Graphs with SPARQL through Natural Language
• LLM-KG-Bench 3.0: A Compass for SemanticTechnology Capabilities in the Ocean of LLMs

Related

• Neural Machine Translating from Natural Language to SPARQL
• SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs
• ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
• A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions
• A Multi-agent Text2SQL Framework using Small Language Models and Execution Feedback