Despite the many advances of Large Language Models (LLMs) and their unprecedented rapid evolution, their impact and integration into every facet of our daily lives is limited due to various reasons. One critical factor hindering their widespread adoption is the occurrence of hallucinations, where LLMs invent answers that sound realistic, yet drift away from factual truth. In this paper, we present a novel method for detecting hallucinations in large language models, which tackles a critical issue in the adoption of these models in various real-world scenarios. Through extensive evaluations across multiple datasets and LLMs, including Llama-2, we study the hallucination levels of various recent LLMs and demonstrate the effectiveness of our method to automatically detect them. Notably, we observe up to 62% hallucinations for Llama-2 in a specific experiment, where our method achieves a Balanced Accuracy (B-ACC) of 87%, all without relying on external knowledge.

翻译：尽管大型语言模型取得了诸多进展并以前所未有的速度演进，但由于多种原因，它们对日常生活的全面影响和融入仍受到限制。阻碍其广泛采用的关键因素之一是幻觉现象，即大型语言模型会编造听起来合理但偏离事实的答案。本文提出了一种检测大型语言模型幻觉的新方法，解决了这些模型在各类实际应用场景中推广的关键问题。通过在多个数据集和大型语言模型（包括Llama-2）上进行广泛评估，我们研究了近期多种大型语言模型的幻觉水平，并展示了我们方法自动检测幻觉的有效性。值得注意的是，在特定实验中，我们观察到Llama-2的幻觉率高达62%，而我们的方法在不依赖外部知识的情况下，实现了87%的平衡准确率。