# Drowzee: Metamorphic Testing for Fact-Conflicting Hallucination Detection in Large Language Models

arXiv: https://arxiv.org/abs/2405.00648 · HTML: https://mlsanity.com/paper/2405.00648/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2024.

## Built on by (most cited first, 14 of 14)

- [RvLLM: LLM Runtime Verification with Domain Knowledge](https://mlsanity.com/paper/2505.18585/) (2025)
- [Metamorphic Testing of Large Language Models for Natural Language Processing](https://mlsanity.com/paper/2511.02108/) (2025)
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- [Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics](https://mlsanity.com/paper/2504.00446/) (2025)
- [Automated Validation of LLM-based Evaluators for Software Engineering Artifacts](https://mlsanity.com/paper/2508.02827/) (2025)
- [APT-Agent: Automated Penetration Testing using Large Language Models](https://mlsanity.com/paper/2605.24949/) (2026)
- [RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation](https://mlsanity.com/paper/2411.03920/) (2024)
- [OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools](https://mlsanity.com/paper/2508.05650/) (2025)
- [Exploring and Mitigating Fawning Hallucinations in Large Language Models](https://mlsanity.com/paper/2509.00869/) (2025)
- [HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing](https://mlsanity.com/paper/2509.23835/) (2025)
- [PCS: Perceived Confidence Scoring of Black Box LLMs with Metamorphic Relations](https://mlsanity.com/paper/2502.07186/) (2025)
- [Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs](https://mlsanity.com/paper/2607.12273/) (2026)
- [MetaSpace: Metamorphic Testing for Spatial Cognition in Embodied Agents](https://mlsanity.com/paper/2608.07533/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have transformed the landscape of language processing, yet struggle with significant challenges in terms of security, privacy, and the generation of seemingly coherent but factually inaccurate outputs, commonly referred to as hallucinations. Among these challenges, one particularly pressing issue is Fact-Conflicting Hallucination (FCH), where LLMs generate content that directly contradicts established facts. Tackling FCH poses a formidable task due to two primary obstacles: Firstly, automating the construction and updating of benchmark datasets is challenging, as current methods rely on static benchmarks that don't cover the diverse range of FCH scenarios. Secondly, validating LLM outputs' reasoning process is inherently complex, especially with intricate logical relations involved. In addressing these obstacles, we propose an innovative approach leveraging logic programming to enhance metamorphic testing for detecting Fact-Conflicting Hallucinations (FCH). Our method gathers data from sources like Wikipedia, expands it with logical reasoning to create diverse test cases, assesses LLMs through structured prompts, and validates their coherence using semantic-aware assessment mechanisms. Our method generates test cases and detects hallucinations across six different LLMs spanning nine domains, revealing hallucination rates ranging from 24.7% to 59.8%. Key observations indicate that LLMs encounter challenges, particularly with temporal concepts, handling out-of-distribution knowledge, and exhibiting deficiencies in logical reasoning capabilities. The outcomes underscore the efficacy of logic-based test cases generated by our tool in both triggering and identifying hallucinations. These findings underscore the imperative for ongoing collaborative endeavors within the community to detect and address LLM hallucinations.