Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning

• URL: http://arxiv.org/abs/2502.13416v1
• Date: Wed, 19 Feb 2025 04:21:46 GMT
• Title: Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning
• Authors: Ningke Li, Yahui Song, Kailong Wang, Yuekang Li, Ling Shi, Yi Liu, Haoyu Wang,
• Abstract summary: Drowzee is an end-to-end metamorphic testing framework for large language models. It uses temporal logic to identify fact-conflicting hallucinations (FCH) in large language models. Results show that Drowzee effectively identifies rates of non-temporal-related hallucinations ranging from 24.7% to 59.8%, and rates of temporal-related hallucinations ranging from 16.7% to 39.2%.
• Score: 10.606613497282398
• License:
• Abstract: Large language models (LLMs) face the challenge of hallucinations -- outputs that seem coherent but are actually incorrect. A particularly damaging type is fact-conflicting hallucination (FCH), where generated content contradicts established facts. Addressing FCH presents three main challenges: 1) Automatically constructing and maintaining large-scale benchmark datasets is difficult and resource-intensive; 2) Generating complex and efficient test cases that the LLM has not been trained on -- especially those involving intricate temporal features -- is challenging, yet crucial for eliciting hallucinations; and 3) Validating the reasoning behind LLM outputs is inherently difficult, particularly with complex logical relationships, as it requires transparency in the model's decision-making process. This paper presents Drowzee, an innovative end-to-end metamorphic testing framework that utilizes temporal logic to identify fact-conflicting hallucinations (FCH) in large language models (LLMs). Drowzee builds a comprehensive factual knowledge base by crawling sources like Wikipedia and uses automated temporal-logic reasoning to convert this knowledge into a large, extensible set of test cases with ground truth answers. LLMs are tested using these cases through template-based prompts, which require them to generate both answers and reasoning steps. To validate the reasoning, we propose two semantic-aware oracles that compare the semantic structure of LLM outputs to the ground truths. Across nine LLMs in nine different knowledge domains, experimental results show that Drowzee effectively identifies rates of non-temporal-related hallucinations ranging from 24.7% to 59.8%, and rates of temporal-related hallucinations ranging from 16.7% to 39.2%.

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