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 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 model from 47.5% to 14.5% on average. 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：近年来开发的大语言模型在生成流利连贯文本方面取得了显著成功。然而，这些模型常常出现"幻觉"现象，严重影响了其可靠性。本研究针对这一关键问题，提出了一种在生成过程中主动检测并缓解幻觉的方法。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，通过验证过程检查其正确性，缓解检测到的幻觉，然后继续生成过程。通过"文章生成任务"的广泛实验，我们首先展示了检测与缓解技术的各自效能。具体而言，检测技术的召回率达到88%，缓解技术成功缓解了57.6%正确检测到的幻觉。重要的是，即使在错误检测到幻觉（即假阳性）的情况下，缓解技术也不会引入新的幻觉。随后，我们表明所提出的主动检测与缓解方法成功将GPT-3模型的平均幻觉率从47.5%降低至14.5%。总之，本研究致力于提升大语言模型的可靠性和可信度，这是推动其在现实应用中广泛采用的关键一步。