Hallucination Detection and Evaluation of Large Language Model

• URL: http://arxiv.org/abs/2512.22416v1
• Date: Sat, 27 Dec 2025 00:17:03 GMT
• Title: Hallucination Detection and Evaluation of Large Language Model
• Authors: Chenggong Zhang, Haopeng Wang,
• Abstract summary: Hallucinations in Large Language Models (LLMs) pose a significant challenge, generating misleading or unverifiable content.<n>Existing evaluation methods, such as KnowHalu, employ multi-stage verification but suffer from high computational costs.<n>To address this, we integrate the Hughes Hallucination Evaluation Model (HHEM), a lightweight classification-based framework.
• Score: 0.26856688022781555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hallucinations in Large Language Models (LLMs) pose a significant challenge, generating misleading or unverifiable content that undermines trust and reliability. Existing evaluation methods, such as KnowHalu, employ multi-stage verification but suffer from high computational costs. To address this, we integrate the Hughes Hallucination Evaluation Model (HHEM), a lightweight classification-based framework that operates independently of LLM-based judgments, significantly improving efficiency while maintaining high detection accuracy. We conduct a comparative analysis of hallucination detection methods across various LLMs, evaluating True Positive Rate (TPR), True Negative Rate (TNR), and Accuracy on question-answering (QA) and summarization tasks. Our results show that HHEM reduces evaluation time from 8 hours to 10 minutes, while HHEM with non-fabrication checking achieves the highest accuracy \(82.2\%\) and TPR \(78.9\%\). However, HHEM struggles with localized hallucinations in summarization tasks. To address this, we introduce segment-based retrieval, improving detection by verifying smaller text components. Additionally, our cumulative distribution function (CDF) analysis indicates that larger models (7B-9B parameters) generally exhibit fewer hallucinations, while intermediate-sized models show higher instability. These findings highlight the need for structured evaluation frameworks that balance computational efficiency with robust factual validation, enhancing the reliability of LLM-generated content.

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