LLMAuditor：一种采用人在回路的大型语言模型审计框架
LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loop
AI总结：
提出LLMAuditor框架，利用不同LLM和人在回路自动生成探测，以审计LLM的偏见、不一致和幻觉，并通过TruthfulQA案例验证其可靠性。
摘要：
随着大型语言模型（LLM）在不同用户和场景中的日益普及，识别使用这些模型时可能存在的问题变得至关重要。此类问题的示例包括：偏见、不一致性和幻觉。虽然对LLM进行这些问题的审计通常是必要的，但这一过程对大多数人而言既不简单也不易获得。一种有效的方法是使用同一问题的不同版本来探测LLM。这可以揭示其知识或操作中的不一致性，表明存在偏见或幻觉的可能性。然而，为了大规模实施这种审计方法，我们需要一种可靠且自动地创建这些探测的方法。在本文中，我们提出了LLMAuditor框架，这是一种自动且可扩展的解决方案，其中使用不同的LLM以及人在回路（HIL）。这种方法提供了可验证性和透明度，同时避免了对同一LLM的循环依赖，并提高了科学严谨性和泛化能力。具体来说，LLMAuditor包括两个人工验证阶段：用于验证响应的标准化评估标准，以及用于生成所需探测的结构化提示模板。一项使用TruthfulQA数据集问题的案例研究表明，我们可以从一个LLM生成一组可靠的探测，用于审计另一个LLM中的不一致性。这一过程通过我们带有HIL的结构化提示模板得到增强，这不仅提高了我们审计方法的可靠性，而且产生了更少幻觉结果。我们研究的新颖性在于开发了一个全面的、通用目的的框架，该框架包括一个经HIL验证的提示模板，用于审计LLM生成的响应。
英文摘要：
As Large Language Models (LLMs) become more pervasive across various users and scenarios, identifying potential issues when using these models becomes essential. Examples of such issues include: bias, inconsistencies, and hallucination. Although auditing the LLM for these problems is often warranted, such a process is neither easy nor accessible for most. An effective method is to probe the LLM using different versions of the same question. This could expose inconsistencies in its knowledge or operation, indicating potential for bias or hallucination. However, to operationalize this auditing method at scale, we need an approach to create those probes reliably and automatically. In this paper we propose the LLMAuditor framework which is an automatic, and scalable solution, where one uses a different LLM along with human-in-the-loop (HIL). This approach offers verifiability and transparency, while avoiding circular reliance on the same LLM, and increasing scientific rigor and generalizability. Specifically, LLMAuditor includes two phases of verification using humans: standardized evaluation criteria to verify responses, and a structured prompt template to generate desired probes. A case study using questions from the TruthfulQA dataset demonstrates that we can generate a reliable set of probes from one LLM that can be used to audit inconsistencies in a different LLM. This process is enhanced by our structured prompt template with HIL, which not only boosts the reliability of our approach in auditing but also yields the delivery of less hallucinated results. The novelty of our research stems from the development of a comprehensive, general-purpose framework that includes a HIL verified prompt template for auditing responses generated by LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
