Lightweight Hallucination Detection for LLMs
A novel entropy-based approach for edge devices
ShED-HD introduces an efficient framework to detect false information in LLM outputs without requiring multiple model runs or external knowledge bases.
- Uses Shannon entropy distribution patterns to identify hallucinations with minimal computational overhead
- Designed specifically for resource-constrained edge devices where traditional methods are too heavy
- Achieves strong detection accuracy while maintaining practical efficiency for real-world applications
- Particularly valuable for security-critical domains where preventing misinformation is essential
This research addresses a critical security challenge by enabling more reliable AI systems in high-stakes environments where factual accuracy is paramount, without requiring cloud connectivity or substantial computing resources.