Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects hallucination in the answers generated by diverse LLMs. Moreover, it performs well in distinguishing hallucination in LLMs' generated answers from both in-distribution and out-of-distribution datasets. Additionally, we also conduct a thorough analysis of the types of hallucinations that occur and present valuable insights. This research significantly contributes to the detection of reliable answers generated by LLMs and holds noteworthy implications for mitigating hallucination in the future work.

通过使用名为RelD的鲁棒性判别器，本文提出了一种有效检测大型语言模型中幻觉问题的方法，并在构建的RelQA双语问答对话数据集上进行了训练。实验结果表明，该方法成功检测到了由不同大型语言模型生成的幻觉回答，且能够区分内部和外部分布数据集中的幻觉问题。此研究为可靠答案的检测做出了重要贡献，并对未来幻觉问题的缓解具有显著的意义。

幻觉检测：在大型语言模型中可靠地区分可信答案