强化人工智能中的伦理边界：提升大型语言模型安全性的高级策略
Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models
AI总结：
本文提出多管齐下的策略，通过过滤敏感词、检测角色扮演、自定义规则引擎及扩展至多模态模型，增强LLM安全性，在保持高性能的同时防止伦理违规和隐私泄露，并引入差异化安全级别以保护用户数据。
摘要：
近年来，大型语言模型（LLMs）的进步显著增强了自然语言处理和人工智能的能力。这些模型，包括GPT-3.5和LLaMA-2，由于变革性的Transformer模型，彻底改变了文本生成、翻译和问答任务。尽管这些模型被广泛使用，但它们也带来了挑战，例如当模型被迫做出不当回应时产生的伦理困境、易受网络钓鱼攻击以及隐私侵犯。本文通过引入一种多管齐下的方法来解决这些挑战，包括：1）过滤用户输入中的敏感词汇以防止不道德的回应；2）检测角色扮演以阻止可能导致“越狱”场景的交互；3）实施自定义规则引擎以限制生成禁止内容；4）将这些方法扩展到各种LLM衍生模型，如多模态大型语言模型（MLLMs）。我们的方法不仅增强了模型抵御不道德操纵和隐私泄露的能力，还保持了它们在高性能任务上的表现。我们在各种攻击提示下展示了最先进的性能，同时不损害模型的核心功能。此外，引入差异化安全级别使用户能够控制其个人数据的披露。我们的方法有助于减少因技术滥用而引发的社会风险和冲突，增强数据保护，并促进社会公平。总的来说，这项研究提供了一个框架，用于平衡问答系统的效率与用户隐私和伦理标准，确保更安全的用户体验并培养对AI技术的信任。
英文摘要：
Recent advancements in large language models (LLMs) have significantly enhanced capabilities in natural language processing and artificial intelligence. These models, including GPT-3.5 and LLaMA-2, have revolutionized text generation, translation, and question-answering tasks due to the transformative Transformer model. Despite their widespread use, LLMs present challenges such as ethical dilemmas when models are compelled to respond inappropriately, susceptibility to phishing attacks, and privacy violations. This paper addresses these challenges by introducing a multi-pronged approach that includes: 1) filtering sensitive vocabulary from user input to prevent unethical responses; 2) detecting role-playing to halt interactions that could lead to 'prison break' scenarios; 3) implementing custom rule engines to restrict the generation of prohibited content; and 4) extending these methodologies to various LLM derivatives like Multi-Model Large Language Models (MLLMs). Our approach not only fortifies models against unethical manipulations and privacy breaches but also maintains their high performance across tasks. We demonstrate state-of-the-art performance under various attack prompts, without compromising the model's core functionalities. Furthermore, the introduction of differentiated security levels empowers users to control their personal data disclosure. Our methods contribute to reducing social risks and conflicts arising from technological abuse, enhance data protection, and promote social equity. Collectively, this research provides a framework for balancing the efficiency of question-answering systems with user privacy and ethical standards, ensuring a safer user experience and fostering trust in AI technology.
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。
