Principled Detection of Hallucinations in Large Language Models via Multiple Testing

2025-08-25

AI Summary

Hallucination detection in large language models (LLMs) lacks theoretical foundations and robustness. Method: This paper formalizes hallucination identification as a statistical hypothesis testing problem, introducing multiple testing theory—specifically Bonferroni-Holm correction—to this domain for the first time, and rigorously establishes its equivalence to out-of-distribution (OOD) detection. The proposed framework leverages confidence-based analysis to yield a theoretically sound, statistically rigorous hallucination detector. Contribution/Results: Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches across diverse LLMs, tasks, and noisy settings, achieving higher detection accuracy and superior robustness. By grounding hallucination detection in classical statistical theory, this work establishes a novel, principled paradigm for trustworthy LLM evaluation.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: Safety and RobustnessData Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphsEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systemsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

While Large Language Models (LLMs) have emerged as powerful foundational models to solve a variety of tasks, they have also been shown to be prone to hallucinations, i.e., generating responses that sound confident but are actually incorrect or even nonsensical. In this work, we formulate the problem of detecting hallucinations as a hypothesis testing problem and draw parallels to the problem of out-of-distribution detection in machine learning models. We propose a multiple-testing-inspired method to solve the hallucination detection problem, and provide extensive experimental results to validate the robustness of our approach against state-of-the-art methods.

Problem

• Detecting hallucinations in large language models
• Formulating hallucination detection as hypothesis testing
• Providing robust method against state-of-the-art approaches

Innovation

• Multiple testing method for hallucination detection
• Formulates detection as hypothesis testing problem
• Validates robustness against state-of-the-art methods