标题： 大型语言模型中幻觉评估的忠实度指标综述 显示英文标题

标题： A review of faithfulness metrics for hallucination assessment in Large Language Models

摘要： 这篇综述考察了在开放式摘要、问答和机器翻译任务中评估忠实性的方法。 我们发现，使用大语言模型作为忠实性评估者通常是与人类判断最高度相关的指标。 讨论了其他研究如何减轻幻觉现象，检索增强生成（RAG）和提示框架方法已被证明与更高的忠实性相关，同时提供了其他缓解建议。 对忠实性的研究对于大语言模型的持续广泛应用至关重要，因为不忠实的回应可能在许多原本适合使用大语言模型的领域带来重大风险。 此外，评估开放式生成提供了一个比常用的选择题基准更全面的衡量标准，这有助于提高对大语言模型的信任。 显示英文摘要

摘要： This review examines the means with which faithfulness has been evaluated across open-ended summarization, question-answering and machine translation tasks. We find that the use of LLMs as a faithfulness evaluator is commonly the metric that is most highly correlated with human judgement. The means with which other studies have mitigated hallucinations is discussed, with both retrieval augmented generation (RAG) and prompting framework approaches having been linked with superior faithfulness, whilst other recommendations for mitigation are provided. Research into faithfulness is integral to the continued widespread use of LLMs, as unfaithful responses can pose major risks to many areas whereby LLMs would otherwise be suitable. Furthermore, evaluating open-ended generation provides a more comprehensive measure of LLM performance than commonly used multiple-choice benchmarking, which can help in advancing the trust that can be placed within LLMs.