Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval
Aditya Sharma, Luis Lara, Amal Zouaq, Christopher J. Pal
2025-02-20
Summary
This paper talks about a surprising discovery in how AI learns from different types of data. The researchers found that even random noise can help AI models learn useful information, which challenges our understanding of how these systems work.
What's the problem?
When teaching AI to work with different types of data (like text and images), we usually think the AI needs to learn from real, labeled examples. But it's not always clear what exactly the AI is learning or transferring between these different types of data. This is especially tricky when we don't have many labeled examples to work with.
What's the solution?
The researchers did a ton of experiments, testing different AI learning methods on over 300 tasks. They found something unexpected: the AI could learn just as well from random noise as it could from real data. They then created a new framework to understand why this works, focusing on how well information can be transferred and distinguished between different types of data.
Why it matters?
This matters because it could change how we train AI systems, especially when we don't have a lot of labeled data to work with. If random noise can be just as helpful as real data in some cases, it might make it easier and cheaper to train AI for new tasks. It also challenges what we thought we knew about how AI learns, which could lead to new and more efficient ways of developing AI systems that can work with many different types of information.
Abstract
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.