Large Language Models (LLMs) have demonstrated remarkable proficiency in generating fluent text. However, they often encounter the challenge of generating inaccurate or hallucinated content. This issue is common in both non-retrieval-based generation and retrieval-augmented generation approaches, and existing post-hoc rectification methods may not address the accumulated hallucination errors that may be caused by the "snowballing" issue, especially in reasoning tasks. To tackle these challenges, we introduce a novel approach called Real-time Verification and Rectification (Ever). Instead of waiting until the end of the generation process to rectify hallucinations, Ever employs a real-time, step-wise generation and hallucination rectification strategy. The primary objective is to detect and rectify hallucinations as they occur during the text generation process. When compared to both retrieval-based and non-retrieval-based baselines, Ever demonstrates a significant improvement in generating trustworthy and factually accurate text across a diverse range of tasks, including short-form QA, biography generation, and multi-hop reasoning.

我们介绍了一种名为Ever的新方法，通过实时检验和矫正策略，解决大语言模型生成的不准确或虚构内容的问题，并在各种任务中，包括短问题回答、传记生成和多跳推理等方面，相比基于检索和非检索的基准模型，明显提高了生成值得信赖和事实准确的文本的能力。

通过实时验证和修正来减轻大型语言模型中的虚构问题