Reducing Hallucinations in Language Model-based SPARQL Query Generation
  Using Post-Generation Memory Retrieval
Authors: name: Aditya Sharma, name: Luis Lara, name: Amal Zouaq, name: Christopher J. Pal

The ability to generate SPARQL queries from natural language questions is
crucial for ensuring efficient and accurate retrieval of structured data from
knowledge graphs (KG). While large language models (LLMs) have been widely
adopted for SPARQL query generation, they are often susceptible to
hallucinations and out-of-distribution errors when producing KG elements like
Uniform Resource Identifiers (URIs) based on internal parametric knowledge.
This often results in content that appears plausible but is factually
incorrect, posing significant challenges for their use in real-world
information retrieval (IR) applications. This has led to increased research
aimed at detecting and mitigating such errors. In this paper, we introduce PGMR
(Post-Generation Memory Retrieval), a modular framework that incorporates a
non-parametric memory module to retrieve KG elements and enhance LLM-based
SPARQL query generation. Our experimental results indicate that PGMR
consistently delivers strong performance across diverse datasets, data
distributions, and LLMs. Notably, PGMR significantly mitigates URI
hallucinations, nearly eliminating the problem in several scenarios.