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\"\"<\/figure>\n\n\n\n

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\"\"<\/figure><\/div>\n\n\n\n

\u4eba\u5de5\u7ea2\u961f\u6d4b\u8bd5\u5927\u6a21\u578b\u8fc7\u7a0b\u7684\u793a\u610f\u56fe<\/p>\n\n\n\n

\u57282019\u5e74\uff0cFacebook AI\u56e2\u961f[3]<\/sup>\u5c31\u5f00\u59cb\u901a\u8fc7\u4eba\u5de5\u4f17\u5305\u65b9\u5f0f\uff0c\u5bf9\u5f53\u65f6\u5148\u8fdb\u7684\u5bf9\u8bdd\u7cfb\u7edf\u5b89\u5168\u8fdb\u884c\u7ea2\u961f\u6d4b\u8bd5\uff0c\u6536\u96c6\u8fdd\u89c4\u6848\u4f8b\uff0c\u5e76\u5c1d\u8bd5\u4fee\u590d\u3002<\/p>\n\n\n\n

\u73b0\u6709\u5927\u6a21\u578b\u5b89\u5168\u57fa\u51c6\u6d4b\u8bd5\u96c6\u4e5f\u4e3b\u8981\u662f\u901a\u8fc7\u4eba\u5de5\u6784\u9020<\/strong>\uff0c\u5305\u62ecMeta\u7684Bot-Adversarial\u6570\u636e\u96c6[6]<\/sup>\u3001\u827e\u4f26\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u6240\uff08Allen Institute for Artificial Intelligence\uff09\u7684RealToxicityPrompts[7]<\/sup>\uff08\u4f5c\u8005\u4e4b\u4e00\u4e3aNLP\u5708\u5de8\u4f6cNoah Smith\uff09\u3001\u6e05\u534e\u7684Safety-Prompts[5]<\/sup>\u3001\u963f\u91cc\u8fbe\u6469\u9662<\/a>\u7684CValues[8]<\/sup>\u7b49\u3002\u4f8b\u5982\uff0c\u963f\u91cc\u8fbe\u6469\u9662\u901a\u8fc7\u9080\u8bf7\u73af\u5883\u79d1\u5b66\u3001\u5fc3\u7406\u5b66\u3001\u6cd5\u7406\u5b66\u7b49\u591a\u4e2a\u9886\u57df\u77e5\u540d\u4e13\u5bb6\uff0c\u5206\u522b\u63d0\u51fa100\u4e2a\u8bf1\u5bfc\u504f\u89c1\u3001\u6b67\u89c6\u56de\u7b54\u7684\u5201\u94bb\u95ee\u9898\u3002JADE<\/strong>\u80fd\u591f\u901a\u8fc7\u8bed\u8a00\u5b66\u53d8\u5f02<\/strong>\uff0c\u5728\u4e0d\u6539\u53d8\u6838\u5fc3\u95ee\u9898\u8bed\u4e49<\/strong>\u7684\u524d\u63d0\u4e0b\uff0c\u8fdb\u4e00\u6b65\u5c06\u8fd9\u4e9b\u624b\u5199\u95ee\u9898\u8f6c\u6362\u4e3a\u9ad8\u5371\u901a\u7528\u6d4b\u8bd5\u95ee\u9898<\/strong>\uff0c\u4f53\u73b0\u4e86JADE\u7684\u9776\u5411\u7279\u6027<\/strong>\u3002<\/p>\n\n\n\n

\"\u56fe\u7247\"\/<\/figure>\n\n\n\n
\"\"<\/figure><\/div>\n\n\n\n

CValues\u5b89\u5168\u6d4b\u8bd5\u96c6\u4e0a\u5927\u6a21\u578b\u7684\u5b89\u5168\u5f97\u5206<\/p>\n\n\n\n

\uff08\u8fdd\u89c4\u7387=100-\u5b89\u5168\u5f97\u5206\uff09 [8]<\/sup><\/p>\n\n\n\n

\u4e0a\u8ff0\u57fa\u51c6\u6d4b\u8bd5\u96c6\u5bf9\u56fd\u5185\u5916\u5927\u6a21\u578b\u521d\u671f\u7684\u5b89\u5168\u5408\u89c4\u80fd\u529b\u63d0\u5347\u975e\u5e38\u5173\u952e\u3002\u622a\u81f3\u76ee\u524d\uff0c\u56fd\u5185\u77e5\u540d\u5927\u6a21\u578b\u5728\u4e0a\u8ff0\u6d4b\u8bd5\u96c6\u4e0a\u7684\u5e73\u5747\u8fdd\u89c4\u7387\u5927\u591a\u4f4e\u4e8e20%<\/strong>\uff0c\u5408\u89c4\u80fd\u529b\u8f83\u5f3a\u7684\u5927\u6a21\u578b\u4eec\u8fdd\u89c4\u7387\u4f4e\u81f32%~5%\u3002\u7b97\u662f\u5b8c\u6210\u4e86\u76f8\u5e94\u7684\u5386\u53f2\u4f7f\u547d\u3002<\/p>\n\n\n\n

\u5de6\u53f3\u4e92\u640f\uff1a\u57fa\u4e8e\u8bed\u8a00\u6a21\u578b\u7684\u7ea2\u961f\u6d4b\u8bd5<\/strong><\/h4>\n\n\n\n

\u4eba\u5de5\u7ea2\u961f\u6d4b\u8bd5\u9700\u8981\u5927\u91cf\u7684\u4eba\u529b\u548c\u91d1\u94b1\u6295\u5165\u3002\u6b64\u5916\uff0c\u968f\u7740\u56fd\u5185\u5916\u5927\u6a21\u578b\u5b89\u5168\u6c34\u4f4d\u4e0d\u65ad\u63d0\u9ad8\uff0c\u4eba\u5de5\u7ea2\u961f\u6d4b\u8bd5\u7684\u8fb9\u9645\u6210\u672c<\/a>\u4e5f\u5728\u6c34\u6da8\u8239\u9ad8<\/strong>\u3002\u8fd9\u4e5f\u662f\u4e3a\u4ec0\u4e48\u4f17\u591a\u5927\u5382\u7eb7\u7eb7\u5f00\u59cb\u7814\u53d1\u57fa\u4e8e\u8bed\u8a00\u6a21\u578b\u751f\u6210\u6d4b\u8bd5\u95ee\u9898\uff0c\u5e0c\u671b\u5b9e\u73b0\u81ea\u52a8\u5316\u7684\u7ea2\u961f\u6d4b\u8bd5\u3002<\/p>\n\n\n\n

\u65e9\u5728GPT-2\u65f6\u4ee3\uff0c\u56fd\u5916\u5df2\u5f00\u59cb\u7814\u7a76\u57fa\u4e8e\u8bed\u8a00\u6a21\u578b\u7684\u7ea2\u961f\u6d4b\u8bd5\u3002Anthropic\u56e2\u961f\u5c31\u66fe\u63d0\u51fa\u5229\u7528\u5f3a\u5316\u5b66\u4e60\u7684\u601d\u60f3\uff0c\u8ba9\u4e00\u4e2aGPT-2\u626e\u6f14\u300c\u6d4b\u8bd5\u5458\u300d<\/strong>\uff0c\u4e0d\u65ad\u4ea7\u751f\u6d4b\u8bd5\u95ee\u9898\uff0c\u5c3d\u53ef\u80fd\u5730\u63d0\u9ad8\u76ee\u6807\u5927\u6a21\u578b\u8f93\u51fa\u8fdd\u89c4\u5185\u5bb9\u7684\u6982\u7387<\/strong>[9]<\/sup>\uff1b\u5fb7\u56fdCISPA\u56e2\u961f\u5219\u5c1d\u8bd5\u8ba9\u5927\u6a21\u578b\u201c\u6d4b\u8bd5\u5458\u201d \u5728\u4e00\u4e9b\u65e0\u5bb3\u5374\u80fd\u89e6\u53d1\u8fdd\u89c4\u7684\u95ee\u9898\u4e0a\u8fdb\u884c\u5fae\u8c03\uff0c\u7814\u7a76\u662f\u5426\u8fd9\u6837GPT-2\u751f\u6210\u7684\u65b0\u95ee\u9898\u80fd\u591f\u89e6\u53d1\u66f4\u591a\u7684\u8fdd\u89c4 [10]<\/sup>\u3002<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u57fa\u4e8e\u8bed\u8a00\u6a21\u578b\u7684\u7ea2\u961f\u6d4b\u8bd5\u578b\u8fc7\u7a0b [3]<\/sup><\/p>\n\n\n\n

\u7136\u800c\uff0c\u5728\u6d4b\u8bd5\u50cfChatGPT<\/a>\u3001LLaMA2\u8fd9\u6837\u5df2\u7ecf\u4eba\u5de5\u5bf9\u9f50\u8fc7\uff08aligned\uff09\u7684\u5927\u6a21\u578b\uff0c\u4e0a\u8ff0\u6280\u672f\u540c\u6837\u9762\u4e34\u5927\u6a21\u578b\u5b89\u5168\u6c34\u4f4d\u6574\u4f53\u4e0a\u5347\u5e26\u6765\u7684\u6311\u6218\u3002<\/p>\n\n\n\n

\u9762\u5bf9ChatGPT\u8fd9\u6837\u7684\u76ee\u6807\uff0c\u5927\u6a21\u578b\u201c\u6d4b\u8bd5\u5458\u201d\u5728\u7ea2\u961f\u521d\u671f\u51e0\u4e4e\u96be\u4ee5\u9020\u6210\u8fdd\u89c4\uff08\u6bd5\u7adf\u4eba\u5de5\u7cbe\u5fc3\u64b0\u5199\u7684\u6d4b\u8bd5\u95ee\u9898\u8fdd\u89c4\u7387\u4e5f\u5f88\u4f4e\uff09\uff0c\u56e0\u6b64\u51e0\u4e4e\u5f97\u4e0d\u5230\u5956\u52b1\uff08reward\uff09\u3002\u8fd9\u662f\u5f3a\u5316\u5b66\u4e60\u4e2d\u7ecf\u5178\u7684\u7a00\u758f\u5956\u52b1<\/strong>\uff08sparse reward\uff09\u95ee\u9898\uff0c\u4f1a\u9020\u6210\u300c\u6d4b\u8bd5\u5458\u300d\u96be\u4ee5\u5b66\u4e60\u5230\u6709\u6548\u7684\u9ad8\u5371\u95ee\u9898\u751f\u6210\u7b56\u7565\u3002\u4eca\u5e746\u6708\uff0cMIT\u56e2\u961f\u5728\u53d1\u5e03\u7684\u57fa\u4e8e\u8bed\u8a00\u6a21\u578b\u7684\u7ea2\u961f\u6d4b\u8bd5\u65b0\u65b9\u6cd5\uff0c\u4e5f\u4ec5\u505a\u5230\u5bf9\u57fa\u5ea7\u5927\u6a21\u578bGPT-3\u7684\u6709\u6548\u6d4b\u8bd5\uff0c\u4e5f\u6ca1<\/strong>\u80fd\u6269\u5c55\u81f3\u4e3b\u6d41\u5bf9\u8bdd\u5f0f\u5927\u6a21\u578b<\/strong>[11]<\/sup>\u3002<\/p>\n\n\n\n

\u535a\u91c7\u4f17\u957f\u7684JADE<\/strong><\/h4>\n\n\n\n

\u4ece\u9ed1\u5ba2\u5f0f\u7684\u72ec\u72fc\u653b\u51fb\u5230\u667a\u80fd\u578b\u7684\u9776\u5411\u5bf9\u6297\uff0c\u5927\u6a21\u578b\u5b89\u5168\u5408\u89c4\u8bc4\u6d4b\u6280\u672f\u5728\u8ffd\u6c42\u9ad8\u6548\u7cbe\u51c6\u7684\u9053\u8def\u4e0a\u5feb\u901f\u53d1\u5c55\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6bd4\u8f83\u73b0\u6709\u5927\u6a21\u578b\u5b89\u5168\u8bc4\u6d4b\u65b9\u6cd5\u7684\u5f02\u540c<\/p>\n\n\n\n

JADE\u5e73\u53f0\u901a\u8fc7\u5f15\u5165\u8bed\u8a00\u5b66\u53d8\u5f02\u7684\u65b0\u601d\u8def\uff0c\u65e0\u9700\u6d4b\u8bd5\u4eba\u5458\u7ede\u5c3d\u8111\u6c41\u5730\u6784\u9020\u80fd\u591f\u89e6\u53d1\u8fdd\u89c4\u7684\u7cbe\u5999\u8868\u8ff0\uff0c\u4ec5\u9700\u8bbe\u5b9a\u60f3\u8981\u6d4b\u8bd5\u7684\u8fdd\u89c4\u4e3b\u9898\uff0c\u9759\u5019\u7247\u523b<\/strong>\uff0c\u4fbf\u53ef\u83b7\u5f97\u76f8\u5e94\u7684\u9ad8\u5371\u53d8\u5f02\u95ee\u9898\uff0c\u63d0\u5347\u4e86\u5927\u6a21\u578b\u5b89\u5168\u8bc4\u6d4b\u6548\u7387\u548c\u81ea\u52a8\u5316\u7a0b\u5ea6\u3002<\/p>\n\n\n\n

\u76f8\u6bd4\u4e8e\u57fa\u4e8e\u8bed\u8a00\u6a21\u578b\u7684\u7ea2\u961f\u6d4b\u8bd5\uff0c\u4f7f\u7528JADE\u8fdb\u884c\u6d4b\u8bd5\u80fd\u591f\u4fdd\u8bc1\u300c<\/strong>\u6240\u6d4b\u5373\u6240\u5f97\u300d<\/strong>\uff0c\u5bf9\u76ee\u6807\u8fdd\u89c4\u4e3b\u9898\u7684\u5b89\u5168\u8fb9\u754c\u8fdb\u884c\u9776\u5411\u5f0f\u63a2\u7d22\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

JADE\u7684\u53cd\u9988-\u8fed\u4ee3\u673a\u5236 \uff1a\u8bed\u8a00\u5b66\u53d8\u5f02+\u5b89\u5168\u5408\u89c4\u8bc4\u6d4b<\/p>\n\n\n\n

\u968f\u7740\u5927\u6a21\u578b\u7684\u5b89\u5168\u6c34\u4f4d\u63d0\u5347\uff0cJADE\u4ecd\u7136\u53ef\u901a\u8fc7\u8c03\u6574\u6d4b\u8bd5\u95ee\u9898\u590d\u6742\u5ea6\u6c34\u4f4d\uff0c\u751f\u6210\u66f4\u5177\u6311\u6218\u6027\u7684\u5b89\u5168\u6d4b\u8bd5\u95ee\u9898\uff0c\u589e\u5f3a\u4e86\u5b89\u5168\u6d4b\u8bd5\u7684\u52a8\u6001\u6027<\/strong>\uff0c\u4ee5\u653b\u4fc3\u9632\uff0c\u5e2e\u52a9\u5927\u6a21\u578b\u5b89\u5168\u5408\u89c4\u80fd\u529b\u6301\u7eed\u63d0\u5347<\/strong>\u3002<\/p>\n\n\n\n

\u56e2\u961f\u7b80\u4ecb<\/strong><\/p>\n\n\n\n

\u590d\u65e6\u767d\u6cfd\u667a\u80fd\u56e2\u961f\u8d1f\u8d23\u4eba\u4e3a\u5f20\u8c27\u6559\u6388\uff0c\u96b6\u5c5e\u4e8e\u6768\u73c9\u6559\u6388\u9886\u8854\u7684\u590d\u65e6\u5927\u5b66\u7cfb\u7edf\u8f6f\u4ef6\u4e0e\u5b89\u5168\u5b9e\u9a8c\u5ba4\u3002\u8be5\u56e2\u961f\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u4e3aAI\u7cfb\u7edf\u5b89\u5168\uff0c\u5305\u62ecAI\u4f9b\u5e94\u94fe\u5b89\u5168\u3001\u6570\u636e\u9690\u79c1\u4e0e\u6a21\u578b\u4fdd\u62a4\u3001\u6a21\u578b\u6d4b\u8bd5\u4e0e\u4f18\u5316\u3001AI\u8d4b\u80fd\u5b89\u5168\u7b49\u7814\u7a76\u65b9\u5411\uff0c\u5728\u5b89\u5168\u9886\u57df\u9876\u4f1a\/\u9876\u520a\u5305\u62ecS&P\u3001USENIX Security\u3001CCS\u3001TDSC\u7b49\uff0c\u4e0eAI\u9886\u57df\u9876\u4f1a\/\u9876\u520a\u5305\u62ecTPAMI\u3001ICML\u3001NeurIPS\u3001ICDE\u3001KDD\u7b49\u53d1\u8868\u8bba\u6587\u6570\u5341\u7bc7\u3002<\/p>\n\n\n\n

\u5f20\u8c27\u6559\u6388\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/mi-zhang-fdu.github.io\/index.chn.html<\/p>\n\n\n\n

\u590d\u65e6\u767d\u6cfd\u667a\u80fd\u56e2\u961f\uff08Whizard AI\uff09\uff1ahttps:\/\/whitzard-ai.github.io\/<\/p>\n\n\n\n

\u76f8\u5173\u94fe\u63a5<\/strong><\/p>\n\n\n\n

[0]https:\/\/www.bilibili.com\/video\/BV11C4y1J7mE<\/p>\n\n\n\n

[1]https:\/\/huggingface.co\/blog\/red-teaming<\/p>\n\n\n\n

[2]https:\/\/learn.microsoft.com\/zh-cn\/azure\/ai-services\/openai\/concepts\/red-teaming<\/p>\n\n\n\n

[3]Dinan, Emily et al. \u201cBuild it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack.\u201d ArXiv abs\/1908.06083 (2019): n. pag.<\/p>\n\n\n\n

[4]Ganguli, Deep et al. \u201cRed Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.\u201d ArXiv abs\/2209.07858 (2022): n. pag.<\/p>\n\n\n\n

[5]Sun, Hao et al. \u201cSafety Assessment of Chinese Large Language Models.\u201d ArXiv abs\/2304.10436 (2023): n. pag.<\/p>\n\n\n\n

[6]Xu, Jing et al. \u201cBot-Adversarial Dialogue for Safe Conversational Agents.\u201d North American Chapter of the Association for Computational Linguistics (2021).<\/p>\n\n\n\n

[7] Gehman, Samuel et al. \u201cRealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models.\u201d Findings (2020).<\/p>\n\n\n\n

[8] Xu, Guohai et al. \u201cCValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility.\u201d ArXiv abs\/2307.09705 (2023): n. pag.<\/p>\n\n\n\n

[9] Perez, Ethan et al. \u201cRed Teaming Language Models with Language Models.\u201d Conference on Empirical Methods in Natural Language Processing (2022).<\/p>\n\n\n\n

[10] Si, Waiman et al. \u201cWhy So Toxic?: Measuring and Triggering Toxic Behavior in Open-Domain Chatbots.\u201d Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (2022): n. pag.<\/p>\n\n\n\n

[11] Casper, Stephen et al. \u201cExplore, Establish, Exploit: Red Teaming Language Models from Scratch.\u201d ArXiv abs\/2306.09442 (2023): n. pag.<\/p>\n\n\n\n

[12]\u88ab\u7f51\u53cb\u6559\u574f \u5fae\u8f6f\u5c11\u5973\u804a\u5929\u673a\u5668\u4eba\u5728Twitter\u4e0a\u98d9\u810f\u8bdd|Tay|\u5fae\u8f6f_\u51e4\u51f0\u79d1\u6280 (ifeng.com)<\/p>\n\n\n\n

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