MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations

Kensuke Mitsuzawa; Damien Garreau

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arXiv · 2506.01367

MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations

Kensuke Mitsuzawa·Damien Garreau

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Abstract

Large Language Models (LLMs) are increasingly integrated into agentic AI systems, yet their propensity to generate hallucinations remains a critical safety concern. Detecting these factual errors at test-time, particularly without ground-truth labels, is essential for building trustworthy autonomous agents. We propose MMD-Flagger, an hallucination detection method that utilizes Maximum Mean Discrepancy (MMD) and monitors the stability of LLM outputs across varying decoding temperatures. Our method tracks the MMD trajectory between a LLM's response at a certain decoding configuration and a set of stochastic samples, identifying hallucinations based on the trajectory's characteristic shape. We evaluate MMDFlagger on multi-lingual claim verification benchmarks (MUCH) using modern LLMs like Llama-3 families and Gemma-3.

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Kensuke Mitsuzawa, Damien Garreau. 2025-06-02. MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations. https://arxiv.org/abs/2506.01367

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