{"id":60822,"date":"2025-06-15T17:21:26","date_gmt":"2025-06-15T09:21:26","guid":{"rendered":"https:\/\/swarma.org\/?p=60822"},"modified":"2025-06-15T17:21:26","modified_gmt":"2025-06-15T09:21:26","slug":"llms%e4%b8%8e%e7%94%9f%e6%88%90%e5%bc%8f%e6%99%ba%e8%83%bd%e4%bd%93%e6%a8%a1%e6%8b%9f%ef%bc%9a%e5%a4%8d%e6%9d%82%e7%b3%bb%e7%bb%9f%e7%a0%94%e7%a9%b6%e7%9a%84%e6%96%b0%e8%8c%83%e5%bc%8f","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=60822","title":{"rendered":"LLMs\u4e0e\u751f\u6210\u5f0f\u667a\u80fd\u4f53\u6a21\u62df\uff1a\u590d\u6742\u7cfb\u7edf\u7814\u7a76\u7684\u65b0\u8303\u5f0f"},"content":{"rendered":"
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\u8bba\u6587\u9898\u76ee\uff1aLLMs and generative agent-based models for complex systems research<\/span><\/span><\/p>\n

\u53d1\u8868\u65f6\u95f4\uff1a2024\u5e7410\u670828\u65e5<\/span><\/span><\/p>\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/doi.org\/10.1016\/j.plrev.2024.10.013<\/span><\/span><\/p>\n

\u4f1a\u8bae\u540d\u79f0\uff1a<\/span><\/span>Physics of Life Reviews<\/span><\/span><\/em><\/p>\n<\/blockquote>\n


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\u8fd1\u5e74\u6765\uff0c\u5927\u8bed\u8a00\u6a21\u578b<\/span>\uff08LLMs\uff09<\/span>\u7684\u7a81\u7834\u6027\u8fdb\u5c55\u6b63\u5728\u91cd\u5851\u81ea\u7136\u79d1\u5b66\u4e0e\u793e\u4f1a\u79d1\u5b66\u7684\u7814\u7a76\u8303\u5f0f\u3002\u8fd1\u671f\uff0c\u53d1\u8868\u5728 <\/span>Physics of Life Reviews<\/span> \u7684\u7efc\u8ff0\u6587\u7ae0\u7cfb\u7edf\u68b3\u7406\u4e86LLMs\u4e0e\u751f\u6210\u5f0f\u667a\u80fd\u4f53\u6a21\u62df<\/span>\uff08Generative Agent-Based Models, GABMs\uff09<\/span>\u5728\u590d\u6742\u7cfb\u7edf\u7814\u7a76\u4e2d\u7684\u524d\u6cbf\u5e94\u7528\u3002\u8fd9\u7c7b\u6a21\u578b\u901a\u8fc7\u5c06LLMs\u5d4c\u5165\u667a\u80fd\u4f53\u51b3\u7b56\u6838\u5fc3\uff0c\u80fd\u591f\u5b9e\u73b0\u4eba\u7c7b\u5728\u590d\u6742\u73af\u5883\u4e2d\u7684\u4e92\u52a8\u884c\u4e3a\u7684\u6a21\u62df\uff0c\u4e3a\u7f51\u7edc\u6f14\u5316\u3001\u7fa4\u4f53\u5408\u4f5c\u3001\u793e\u4f1a\u52a8\u6001\u4f20\u64ad\u7b49\u7ecf\u5178\u95ee\u9898\u63d0\u4f9b\u4e86\u5168\u65b0\u7814\u7a76\u8def\u5f84\u3002LLMs\u4e0d\u4ec5\u80fd\u590d\u73b0\u4eba\u7c7b\u7684\u516c\u5e73\u6027\u3001\u5408\u4f5c\u503e\u5411\u7b49\u793e\u4f1a\u89c4\u8303\uff0c\u8fd8\u5c55\u73b0\u51fa\u6210\u672c\u6548\u76ca\u3001\u53ef\u6269\u5c55\u6027\u548c\u4f26\u7406\u7b80\u5316\u7b49\u72ec\u7279\u4f18\u52bf\u3002\u7136\u800c\uff0c\u5176\u884c\u4e3a\u7684\u4e0d\u4e00\u81f4\u6027\u3001\u63d0\u793a\u654f\u611f\u6027\u4ee5\u53ca\u201c\u5e7b\u89c9\u201d\u95ee\u9898\u4e5f\u63ed\u793a\u4e86AI\u667a\u80fd\u4f53\u63a7\u5236\u7684\u6280\u672f\u6311\u6218\u3002<\/span><\/span><\/p>\n


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\u56fe 1. \u751f\u6210\u5f0f\u667a\u80fd\u4f53\u6a21\u578b\uff08GABMs\uff09\u3002\u667a\u80fd\u4f53\u5e76\u4e0d\u6839\u636e\u4e00\u7ec4\u56fa\u5b9a\u7684\u89c4\u5219\u6765\u51b3\u5b9a\u5b83\u4eec\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u3002\u76f8\u53cd\uff0c\u4f1a\u5411LLM\u53d1\u9001\u5305\u542b\u6240\u9700\u8be6\u7ec6\u4fe1\u606f\u7684\u63d0\u793a\uff0c\u5e76\u8fd4\u56de\u667a\u80fd\u4f53\u5e94\u8be5\u505a\u51fa\u7684\u51b3\u7b56\u3002<\/span><\/span><\/p>\n


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\u56fe3. \u63d0\u793a\u4e00\u4e2aLLM\u53c2\u4e0e\u72ec\u88c1\u8005\u535a\u5f08\u3002\u5c06Brookins\u7b49\u4eba\u63d0\u4f9b\u7684\u6307\u4ee4\u590d\u5236\u5230\u4f7f\u7528GPT-3.5\u7684LLM\u667a\u80fd\u4f53\u4e2d\u3002\u867d\u7136\u6307\u4ee4\u6ca1\u6709\u660e\u786e\u63d0\u5230\u516c\u5e73\uff0c\u4f46LLM\u8868\u73b0\u51fa\u516c\u5e73\u5206\u914d\u7684\u503e\u5411\uff0c\u8d85\u8fc7\u4eba\u7c7b\u53c2\u4e0e\u8005\u3002<\/span><\/span><\/p>\n


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\u793e\u4f1a\u52a8\u529b\u5b66\uff1a\u4eba\u673a\u6df7\u5408\u51b3\u7b56\u7684\u66d9\u5149<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u793e\u4f1a\u52a8\u6001\u6a21\u62df\u65b9\u9762\uff0cLLMs\u6b63\u5728\u7a81\u7834\u4f20\u7edf\u667a\u80fd\u4f53\u6a21\u578b\u7684\u5c40\u9650\u3002\u4e00\u9879\u7814\u7a76\u5728\u6d41\u884c\u75c5\u5efa\u6a21\u4e2d\u6784\u5efa\u4e86\u4e09\u5c42\u63d0\u793a\u6846\u67b6\uff1a\u57fa\u7840\u573a\u666f\u667a\u80fd\u4f53\u4ec5\u8003\u8651\u5de5\u4f5c\u6536\u5165\uff1b\u5065\u5eb7\u53cd\u9988\u5c42\u52a0\u5165\u4e2a\u4f53\u75c7\u72b6\u611f\u77e5\uff1b\u5b8c\u6574\u53cd\u9988\u5c42\u8fdb\u4e00\u6b65\u5f15\u5165\u793e\u533a\u611f\u67d3\u6570\u636e\u3002\u7ed3\u679c\u663e\u793a\uff0c<\/span><\/span>\u83b7\u53d6\u7fa4\u4f53\u4fe1\u606f\u7684\u667a\u80fd\u4f53\u81ea\u53d1\u4ea7\u751f\u5c45\u5bb6\u9694\u79bb\u884c\u4e3a\uff0c\u4f7f\u75ab\u60c5\u4f20\u64ad\u89c4\u6a21\u4e0b\u964d80%<\/span><\/span><\/strong>\u3002\u8fd9\u79cd\u65e0\u9700\u9884\u8bbe\u89c4\u5219\u7684\u9002\u5e94\u6027\u51b3\u7b56\uff0c\u4e3a\u516c\u5171\u536b\u751f\u5e72\u9884\u6a21\u62df\u63d0\u4f9b\u4e86\u52a8\u6001\u54cd\u5e94\u57fa\u7840\u3002<\/span><\/span><\/p>\n


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<\/section>\n

\u56fe5. LLM\u9a71\u52a8\u667a\u80fd\u4f53\u7684\u75ab\u60c5\u4f20\u64ad\u6a21\u578b\u3002Williams\u7b49\u4eba\u63d0\u51fa\u4e00\u79cd\u6a21\u578b\uff0c\u5176\u4e2d\u4e2a\u4f53\u6bcf\u5929\u901a\u8fc7LLM\u51b3\u7b56\u662f\u5426\u5916\u51fa\u4e92\u52a8\u6216\u5c45\u5bb6\u9694\u79bb\u3002\u5728\u57fa\u51c6\u573a\u666f\u4e2d\uff0cLLM\u4ec5\u88ab\u544a\u77e5\u4e2a\u4f53\u9700\u8981\u5de5\u4f5c\u8d5a\u94b1\uff1b\u5728\u5065\u5eb7\u72b6\u6001\u53cd\u9988\u573a\u666f\u4e2d\uff0c\u63d0\u793a\u8bcd\u989d\u5916\u5305\u542b\u4e2a\u4f53\u7684\u5065\u5eb7\u72b6\u51b5\uff1b\u6700\u7ec8\uff0c\u5b8c\u6574\u53cd\u9988\u573a\u666f\u8fd8\u4f1a\u63d0\u4f9b\u793e\u533a\u75c5\u6bd2\u4f20\u64ad\u4fe1\u606f\uff08\u7814\u7a76\u8005\u5c06\u5176\u547d\u540d\u4e3a"Catasat\u75c5\u6bd2"\uff09\u53ca\u524d\u4e00\u9636\u6bb5\u540c\u533a\u57df\uff08\u540d\u4e3a"Dewberry Hollow"\uff09\u7684\u611f\u67d3\u4eba\u6570\u6570\u636e\u3002<\/span><\/span><\/p>\n


<\/span><\/p>\n

\u800c\u5728\u7fa4\u4f53\u51b3\u7b56\u9886\u57df\uff0c\u53e6\u4e00\u9879\u7814\u7a76\u8bc1\u5b9eGPT-3\u80fd\u7cbe\u51c6\u6a21\u62df\u7279\u5b9a\u4e9a\u6587\u5316\u7fa4\u4f53<\/span>\uff08\u5982\u7f8e\u56fd\u798f\u97f3\u6d3e\u57fa\u7763\u5f92\uff09<\/span>\u7684\u653f\u6cbb\u503e\u5411\uff0c\u5176\u9884\u6d4b\u7ed3\u679c\u4e0e\u771f\u5b9e\u6c11\u8c03\u8bef\u5dee\u5c0f\u4e8e2%\u3002\u8fd9\u79cd\u7ec6\u7c92\u5ea6\u7684\u4eba\u7fa4\u5efa\u6a21\u80fd\u529b\uff0c\u4f7f\u653f\u7b56\u4eff\u771f\u53ef\u4ee5\u89e6\u53ca\u4f20\u7edf\u95ee\u5377\u8c03\u67e5\u96be\u4ee5\u8986\u76d6\u7684\u5fae\u89c2\u793e\u4f1a\u7ed3\u6784\u3002<\/span><\/span><\/p>\n


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<\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


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\u5c3d\u7ba1\u524d\u666f\u5e7f\u9614\uff0cLLMs\u5728\u590d\u6742\u7cfb\u7edf\u4e2d\u7684\u5e94\u7528\u4ecd\u9762\u4e34\u4e09\u91cd\u6311\u6218\u3002<\/span><\/span>\u9996\u5148\u662f\u63d0\u793a\u5de5\u7a0b\u7684\u8106\u5f31\u6027<\/span><\/span><\/strong>\uff1a\u5728\u6700\u540e\u901a\u7252\u535a\u5f08<\/span>\uff08Ultimatum Game\uff09<\/span>\u4e2d\uff0c\u540c\u4e00LLM\u4f5c\u4e3a\u63d0\u8bae\u8005\u4e0e\u54cd\u5e94\u8005\u65f6\u884c\u4e3a\u4e00\u81f4\u6027\u4ec550%\uff0c\u800c\u53cc\u667a\u80fd\u4f53\u67b6\u6784\u5219\u63d0\u5347\u81f388%\uff0c\u663e\u793a\u51b3\u7b56\u89d2\u8272\u7684\u8bed\u4e49\u9694\u79bb\u81f3\u5173\u91cd\u8981\u3002<\/span><\/span><\/p>\n


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\u5176\u6b21\u662f\u4ef7\u503c\u89c2\u7684\u9690\u5f62\u7f16\u7801<\/span><\/span><\/strong>\uff0c\u5728\u4e00\u9879\u653f\u6cbb\u503e\u5411\u91cf\u8868\u68c0\u6d4b\u4e2d\u53d1\u73b0\uff0c\u65e9\u671f\u6a21\u578b<\/span>\uff08\u5982BERT\uff09<\/span>\u5448\u73b0\u4fdd\u5b88\u503e\u5411\uff0c\u800cGPT-4\u5219\u66f4\u8d8b\u81ea\u7531\u4e3b\u4e49\uff0c\u8fd9\u79cd\u504f\u5dee\u53ef\u80fd\u626d\u66f2\u793e\u4f1a\u6a21\u62df\u7684\u5ba2\u89c2\u6027\u3002<\/span><\/span><\/p>\n


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\u6700\u540e\u662f\u7fa4\u4f53\u6d8c\u73b0\u7684\u4e0d\u786e\u5b9a\u6027\uff0c<\/span><\/span><\/strong>\u5728\u591a\u667a\u80fd\u4f53\u516c\u5171\u54c1\u535a\u5f08\u4e2d\u53d1\u73b0\uff0c\u6076\u610f\u8282\u70b9\u7684\u5b58\u5728\u4f1a\u4f7f\u76f8\u90bb\u4e2a\u4f53\u8d21\u732e\u7387\u4e0b\u964d40%\uff0c\u4f46\u53e6\u4e00\u56e2\u961f\u89c2\u5bdf\u5230\u8865\u507f\u6027\u5408\u4f5c\u589e\u5f3a\uff0c\u8fd9\u79cd\u77db\u76fe\u53ef\u80fd\u6e90\u4e8e\u4e0d\u540cLLM\u7248\u672c\u7684\u98ce\u9669\u8bc4\u4f30\u673a\u5236\u5dee\u5f02\u3002<\/span><\/span><\/p>\n


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<\/section>\n

\u56fe6. LLM\u5728\u793e\u4f1a\u51b3\u7b56\u4e2d\u7684\u6574\u5408\u5e94\u7528\u3002\u5de6\u56fe\uff1a\u667a\u80fd\u4f53\u4e3a\u653f\u5e9c\u6216\u7b2c\u4e09\u65b9\u7ec4\u7ec7\u63d0\u4f9b\u51b3\u7b56\u652f\u6301\uff0c\u53f3\u56fe\uff1a\u667a\u80fd\u4f53\u8f85\u52a9\u4e2a\u4eba\u51b3\u7b56\u7684\u8fc7\u7a0b\u3002\u6b64\u7c7b\u667a\u80fd\u5143\u7d20\u7684\u6574\u5408\u673a\u5236\uff0c\u5728\u6982\u5ff5\u4e0a\u7c7b\u4f3c\u4e8e\u9886\u57df\u4e13\u5bb6\u59d4\u5458\u4f1a\u6a21\u5f0f\uff0c\u4f46\u5176\u89c4\u6a21\u53ef\u6269\u5c55\u81f3\u524d\u6240\u672a\u6709\u7684\u6c34\u5e73\u3002<\/span><\/span><\/p>\n


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\u56e0\u6b64\uff0c\u672a\u6765\u7684\u7814\u7a76\u9700\u5efa\u7acb\u6807\u51c6\u5316\u7684\u63d0\u793a\u8bcd\u534f\u8bae\u3001\u5f00\u53d1\u504f\u5dee\u68c0\u6d4b\u5de5\u5177\uff0c\u5e76\u6df1\u5165\u63a2\u7d22\u4eba\u673a\u6df7\u5408\u7fa4\u4f53\u4e2d\u65b0\u578b\u5408\u4f5c\u673a\u5236\u7684\u6d8c\u73b0\u89c4\u5f8b\u3002\u5f53\u6570\u5341\u4ebfLLM\u667a\u80fd\u4f53\u5728\u6570\u5b57\u7a7a\u95f4\u6301\u7eed\u4ea4\u4e92\uff0c\u53ef\u80fd\u50ac\u751f\u8d85\u8d8a\u4eba\u7c7b\u7ecf\u9a8c\u7684\u793e\u4f1a\u52a8\u529b\u5b66\u65b0\u6a21\u5f0f\u3002\u8fd9\u573a\u59cb\u4e8e\u8bed\u8a00\u7406\u89e3\u7684\u9769\u547d\uff0c\u672a\u6765\u53ef\u80fd\u5c06\u91cd\u5851\u6211\u4eec\u7406\u89e3\u590d\u6742\u6027\u7684\u65b9\u5f0f\u3002<\/span><\/span><\/p>\n


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\u53c2\u8003\u6587\u732e<\/span><\/span><\/p>\n

[1] De Marzo, G., Pietronero, L., & Garcia, D. (2023). Emergence of scale-free networks in social interactions among large language models. <\/span><\/span>arXiv preprint arXiv:2312.06619<\/span><\/span><\/em>.<\/span><\/span><\/p>\n

[2] Guo, F. (2023). GPT in game theory experiments. <\/span><\/span>arXiv preprint arXiv:2305.05516<\/span><\/span><\/em>.<\/span><\/span><\/p>\n

[3] Williams, R., Hosseinichimeh, N., Majumdar, A., & Ghaffarzadegan, N. (2023). Epidemic modeling with generative agents. <\/span><\/span>arXiv preprint arXiv:2307.04986<\/span><\/span><\/em>.<\/span><\/span><\/p>\n

[4] Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., & Wingate, D. (2023). Out of one, many: Using language models to simulate human samples. <\/span><\/span>Political Analysis<\/span><\/span><\/em>, <\/span><\/span>31<\/span><\/span><\/em>(3), 337-351.<\/span><\/span><\/p>\n


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\u300c\u5927\u6a21\u578b\u65f6\u4ee3\u4e0b\u7684Agent\u5efa\u6a21\u4e0e\u4eff\u771f\u300d\u8bfb\u4e66\u4f1a<\/span><\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


<\/span><\/span><\/span><\/strong><\/span><\/section>\n

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\"\u56fe\u7247\"<\/span><\/a><\/p>\n

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<\/span><\/span><\/strong><\/strong><\/strong><\/section>\n
\u63a8\u8350\u9605\u8bfb<\/span><\/span><\/strong><\/strong><\/strong><\/section>\n
<\/strong><\/section>\n
1. \u751f\u6210\u5f0f\u667a\u80fd\u4f53\uff1a\u4eba\u7c7b\u884c\u4e3a\u4ea4\u4e92\u5f0f\u6a21\u62df<\/span><\/a><\/span><\/strong>
<\/span><\/strong><\/span><\/section>\n
2. \u65af\u5766\u798f\u3001\u8c37\u6b4c\u53d1\u5e03\u751f\u6210\u5f0f\u4ee3\u7406\uff1a25\u4e2aAI\u667a\u80fd\u4f53\uff0c\u5728\u865a\u62df\u5c0f\u9547\u81ea\u7531\u6210\u957f<\/span><\/a><\/span><\/strong>
<\/span><\/strong><\/span><\/section>\n
3. Nature\uff1a\u751f\u6210\u5f0fAI\u6a21\u578b\u5982\u4f55\u7528\u4e8e\u8f85\u52a9\u4eba\u7c7b\u521b\u610f\u8bbe\u8ba1<\/span><\/a><\/span><\/strong><\/section>\n
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4. <\/span><\/strong><\/span>\u6d8c\u73b0\u52a8\u529b\u5b66\u5982\u4f55\u7528\u6765\u5206\u6790\u590d\u6742\u7cfb\u7edf\uff1f | \u65b0\u8bfe\u4e0a\u7ebf<\/a><\/span><\/strong><\/strong><\/span><\/section>\n
<\/strong>5. <\/span><\/strong>AI\u65f6\u4ee3\u7684\u5b66\u4e60\uff1a\u5171\u63a2\u4eba\u7c7b\u5b66\u4e60\u7684\u590d\u6742\u6027<\/span><\/a><\/span><\/strong><\/section>\n

6. <\/span><\/strong>\u63a2\u7d22\u8005\u8ba1\u5212 | \u96c6\u667a\u4ff1\u4e50\u90e82025\u5185\u5bb9\u56e2\u961f\u62db\u52df\uff08\u5168\u804c&\u517c\u804c\uff09<\/a><\/span><\/strong><\/span><\/p>\n


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