大型语言模型真的无偏吗？用于评估对抗性鲁棒性以应对偏见诱导的越狱提示
Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitation
AI总结：
本研究探究最新大型语言模型（LLMs）中的固有偏见，测试其对偏见诱导越狱提示的对抗性鲁棒性，通过实验证实LLMs仍可被操纵生成偏见响应，强调需增强缓解技术以提升AI公平性与可靠性。
摘要：
大型语言模型（LLMs）彻底改变了人工智能，展现出卓越的计算能力和语言能力。然而，这些模型因训练数据而固有地倾向于各种偏见，包括选择、语言和确认偏见，以及与性别、种族、性取向、宗教、社会经济地位、残疾和年龄相关的常见刻板印象。本研究探究了最新LLMs响应中这些偏见的存在，分析其对公平性和可靠性的影响。我们还研究了如何利用已知的提示工程技术有效揭示LLMs的隐藏偏见，测试它们对专门为偏见诱导设计的越狱提示的对抗性鲁棒性。我们使用不同规模的最广泛LLMs进行了大量实验，证实尽管LLMs具备先进能力和复杂对齐过程，仍可被操纵以生成有偏见或不适当的响应。我们的发现强调了增强缓解技术以解决这些安全问题的重要性，旨在实现更可持续和包容的人工智能。
英文摘要：
Large Language Models (LLMs) have revolutionized artificial intelligence, demonstrating remarkable computational power and linguistic capabilities. However, these models are inherently prone to various biases stemming from their training data. These include selection, linguistic, and confirmation biases, along with common stereotypes related to gender, ethnicity, sexual orientation, religion, socioeconomic status, disability, and age. This study explores the presence of these biases within the responses given by the most recent LLMs, analyzing the impact on their fairness and reliability. We also investigate how known prompt engineering techniques can be exploited to effectively reveal hidden biases of LLMs, testing their adversarial robustness against jailbreak prompts specially crafted for bias elicitation. Extensive experiments are conducted using the most widespread LLMs at different scales, confirming that LLMs can still be manipulated to produce biased or inappropriate responses, despite their advanced capabilities and sophisticated alignment processes. Our findings underscore the importance of enhancing mitigation techniques to address these safety issues, toward a more sustainable and inclusive artificial intelligence.
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。
