ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices

Hallucinations in AI are a serious problem in high-stakes domains: Large language models can produce content that sounds plausible but is factually incorrect, which poses particular risks in fields like healthcare and law where accuracy is critical.

Existing solutions force a difficult trade-off between speed and accuracy: Current hallucination detection methods either require running the AI model multiple times (which is computationally expensive) or use faster single-pass approaches that sacrifice accuracy—neither option works well on resource-limited devices like smartphones or edge computers.

ShED-HD offers a lightweight alternative that works on edge devices: The proposed method uses a small neural network architecture (BiLSTM with single-headed attention) to detect hallucinations by analyzing uncertainty patterns in AI-generated text, enabling hallucination detection on devices with limited computing power without requiring multiple inference passes.

The approach shows strong performance across different scenarios: Testing on three different datasets (BioASQ, TriviaQA, and Jeopardy Questions) demonstrated that ShED-HD significantly outperforms other efficient methods, particularly when encountering new types of data it hasn't seen before, while maintaining competitive accuracy on familiar data.

This enables trustworthy AI in resource-constrained environments: By making accurate hallucination detection feasible on edge devices, this work helps bring responsible AI safeguards to real-world applications where computational resources are limited but trustworthiness is essential.