How Far Can You Get Without a GPU? A Systematic Benchmark of Lightweight Hallucination Detection Across Question Answering, Dialogue, and Summarisation
arXiv:2606.29809v1 Announce Type: cross Abstract: Hallucination detection has become a pressing requirement for trustworthy AI deployment at scale. The most accurate detection methods depend on GPU-intensive inference, proprietary API calls, or white-box access to the generating model. This puts them out of reach for resource-constrained researchers and practitioners. In this paper, we explore a practical alternative: how well can hallucination detection perform using only lightweight, CPU-feasible methods built on publicly available models? We systematically benchmark five such methods: ROUGE
The proliferation of AI models makes reliable and accessible hallucination detection a critical and immediate need for wider, trustworthy AI deployment.
This research provides a pathway for resource-constrained entities to implement vital AI safety measures, democratizing access to crucial AI assurance tools.
The ability to perform effective hallucination detection without requiring high-end GPUs or proprietary models makes robust AI deployment more feasible for a broader range of developers and organizations.
- · Resource-constrained AI researchers and practitioners
- · Open-source AI development
- · Trustworthy AI initiatives
- · SME AI developers
- · Companies offering GPU-intensive AI validation tools
- · Developers solely reliant on proprietary AI APIs
Widespread adoption of lightweight hallucination detection tools enables more pervasive and safer AI integration.
This could accelerate the deployment of AI in sectors with limited compute resources or strict data privacy requirements, such as government and healthcare.
Reduced barriers to entry for AI safety and trustworthiness could foster greater public confidence and broader societal acceptance of AI systems.
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Read at arXiv cs.AI