Recently developed large language models have achieved remarkable success in generating fluent and coherent text. However, these models often tend to 'hallucinate' which critically hampers their reliability. In this work, we address this crucial problem and propose an approach that actively detects and mitigates hallucinations during the generation process. Specifically, we first identify the candidates of potential hallucination leveraging the model's logit output values, check their correctness through a validation procedure, mitigate the detected hallucinations, and then continue with the generation process. Through extensive experiments with GPT-3.5 (text-davinci-003) on the 'article generation task', we first demonstrate the individual efficacy of our detection and mitigation techniques. Specifically, the detection technique achieves a recall of ~88% and the mitigation technique successfully mitigates 57.6% of the correctly detected hallucinations. Importantly, our mitigation technique does not introduce new hallucinations even in the case of incorrectly detected hallucinations, i.e., false positives. Then, we show that the proposed active detection and mitigation approach successfully reduces the hallucinations of the GPT-3.5 model from 47.5% to 14.5% on average. We further demonstrate the effectiveness and wide applicability of our approach through additional studies including performance on different types of questions (multi-hop and false premise questions) and with another LLM from a different model family (Vicuna). In summary, our work contributes to improving the reliability and trustworthiness of large language models, a crucial step en route to enabling their widespread adoption in real-world applications.

翻译：近期开发的大语言模型在生成流畅连贯文本方面取得了显著成功。然而，这些模型常出现“幻觉”现象，严重损害其可靠性。本文针对这一关键问题，提出了一种在生成过程中主动检测与缓解幻觉的方法。具体而言，我们首先利用模型的对数输出值识别潜在幻觉候选对象，通过验证流程检查其正确性，缓解已检测到的幻觉，然后继续执行生成过程。通过在GPT-3.5（text-davinci-003）上针对“文章生成任务”开展广泛实验，我们首先验证了检测与缓解技术各自的有效性：检测技术的召回率达约88%，缓解技术成功修正了57.6%的正确检测幻觉。重要的是，即使出现错误检测幻觉（即假阳性），缓解技术也不会引入新幻觉。随后，我们证明该主动检测与缓解方法成功将GPT-3.5模型的平均幻觉率从47.5%降至14.5%。通过额外研究（包括针对不同类型问题（多跳问题与错误前提问题）的性能测试，以及使用另一模型家族的大语言模型Vicuna进行的验证），我们进一步展示了方法的有效性与广泛适用性。总之，本研究为提高大语言模型的可靠性与可信度做出了贡献，这是推动其在现实应用场景中广泛部署的关键步骤。