细化输入护栏：通过思维链微调与对齐提升LLM作为评判者的效率
Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment
AI总结：
本研究通过微调与对齐LLM的思维链响应，将其作为输入审核护栏，以检测恶意输入并提升对话AI系统的安全性与可靠性。
摘要：
大型语言模型（LLMs）已展现出强大的能力，使其在包括对话式AI产品在内的各种应用中具有重要价值。确保这些产品的安全性和可靠性至关重要，需要减轻其对恶意用户交互的脆弱性，因为此类交互可能导致重大风险暴露和声誉损害。在本工作中，我们提出了一项关于微调和对齐不同LLM的思维链（CoT）响应对输入审核护栏有效性的全面研究。我们通过利用少量训练数据系统地探索各种调优方法，将这些模型适配为代理防御机制，以检测恶意输入并为其裁决提供推理依据，从而防止对话代理被利用。我们严格评估了不同调优策略在跨多种对抗性和恶意查询类型中的泛化效果与鲁棒性。我们的实验结果概述了针对各种有害输入查询进行对齐过程的潜力，即使在数据资源受限的情况下也是如此。这些技术显著增强了对话式AI系统的安全性，并为部署更安全、更可信的AI驱动交互提供了一个可行的框架。
英文摘要：
Large Language Models (LLMs) have demonstrated powerful capabilities that render them valuable in different applications, including conversational AI products. It is paramount to ensure the security and reliability of these products by mitigating their vulnerabilities towards malicious user interactions, which can lead to the exposure of great risks and reputational repercussions. In this work, we present a comprehensive study on the efficacy of fine-tuning and aligning Chain-of-Thought (CoT) responses of different LLMs that serve as input moderation guardrails. We systematically explore various tuning methods by leveraging a small set of training data to adapt these models as proxy defense mechanisms to detect malicious inputs and provide a reasoning for their verdicts, thereby preventing the exploitation of conversational agents. We rigorously evaluate the efficacy and robustness of different tuning strategies to generalize across diverse adversarial and malicious query types. Our experimental results outline the potential of alignment processes tailored to a varied range of harmful input queries, even with constrained data resources. These techniques significantly enhance the safety of conversational AI systems and provide a feasible framework for deploying more secure and trustworthy AI-driven interactions.
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。
