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.

翻译：大语言模型（LLMs）中的幻觉问题构成了一项重大挑战，其生成的误导性或不可验证内容会损害模型的信任度与可靠性。现有评估方法（如KnowHalu）采用多阶段验证机制，但存在计算成本高昂的缺陷。为此，我们整合了休斯幻觉评估模型（HHEM），这是一个基于分类的轻量级框架，其运行独立于基于LLM的判断，在保持高检测精度的同时显著提升了效率。我们对多种LLM的幻觉检测方法进行了对比分析，在问答（QA）和摘要生成任务上评估了真阳性率（TPR）、真阴性率（TNR）及准确率。实验结果表明，HHEM将评估时间从8小时缩短至10分钟，而结合非虚构性检查的HHEM实现了最高的准确率（82.2%）和真阳性率（78.9%）。然而，HHEM在处理摘要任务中的局部幻觉时仍存在困难。针对此问题，我们引入了基于片段的检索机制，通过验证更小的文本单元来提升检测性能。此外，我们的累积分布函数（CDF）分析表明，较大参数规模（7B-9B）的模型通常表现出更少的幻觉，而中等规模的模型则呈现更高的不稳定性。这些发现凸显了需要构建结构化评估框架，以在计算效率与强健的事实验证之间取得平衡，从而提升LLM生成内容的可靠性。