{"id":60214,"date":"2025-05-30T19:20:20","date_gmt":"2025-05-30T11:20:20","guid":{"rendered":"https:\/\/swarma.org\/?p=60214"},"modified":"2025-05-30T19:20:20","modified_gmt":"2025-05-30T11:20:20","slug":"%e7%a0%b4%e8%a7%a3ai%e9%bb%91%e7%ae%b1%e7%9a%84%e5%9b%9b%e9%87%8d%e8%a7%86%e8%a7%92%ef%bc%9a%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%8f%af%e8%a7%a3%e9%87%8a%e6%80%a7%e8%af%bb%e4%b9%a6%e4%bc%9a%e5%90%af%e5%8a%a8","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=60214","title":{"rendered":"\u7834\u89e3AI\u9ed1\u7bb1\u7684\u56db\u91cd\u89c6\u89d2\uff1a\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u8bfb\u4e66\u4f1a\u542f\u52a8"},"content":{"rendered":"
\n
    <\/ol>\n
    \n
    \n
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    \n
    <\/span><\/section>\n
    \n
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    \n

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

    \u5bfc\u8bed<\/span><\/strong><\/p>\n<\/section>\n<\/section>\n

    \n

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

    \u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u526f\u6559\u6388\u5f20\u62f3\u77f3\u3001\u963f\u91cc\u4e91\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u56e2\u961f\u8d1f\u8d23\u4eba\u6c88\u65ed\u3001\u5f69\u4e91\u79d1\u6280\u9996\u5e2d\u79d1\u5b66\u5bb6\u8096\u8fbe\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7855\u58eb\u751f\u6768\u660e\u54f2\u548c\u6d59\u6c5f\u5927\u5b66\u535a\u58eb\u751f\u59da\u4e91\u5fd7\u5171\u540c\u53d1\u8d77\u300c\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u300d\u8bfb\u4e66\u4f1a\u3002\u672c\u8bfb\u4e66\u4f1a\u65e8\u5728\u7a81\u7834\u5927\u6a21\u578b\u201c\u9ed1\u7bb1\u201d\u56f0\u5883\uff0c\u5c1d\u8bd5\u4ece\u4ee5\u4e0b\u56db\u4e2a\u89c6\u89d2\u68b3\u7406\u5927\u8bed\u8a00\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u7684\u79d1\u5b66\u65b9\u6cd5\u8bba\uff1a<\/span><\/span><\/p>\n

    \u81ea\u4e0b\u800c\u4e0a\uff1aTransformer circuit \u4e3a\u4ec0\u4e48\u6709\u6548\uff1f<\/span><\/span><\/p>\n

    \u81ea\u4e0a\u800c\u4e0b\uff1a\u795e\u7ecf\u7f51\u7edc\u7684\u7cbe\u7ec6\u51b3\u7b56\u903b\u8f91\u548c\u6027\u80fd\u6839\u56e0\u662f\u5426\u53ef\u4ee5\u88ab\u4e25\u8c28\u3001\u6e05\u6670\u5730\u89e3\u91ca\u6e05\u695a\uff1f<\/span><\/span><\/p>\n

    \u590d\u6742\u79d1\u5b66\uff1a\u6e17\u6d41\u76f8\u53d8\u3001\u6d8c\u73b0\u3001\u81ea\u7ec4\u7ec7\u7b49\u590d\u6742\u79d1\u5b66\u7406\u8bba\u5982\u4f55\u7406\u89e3\u5927\u6a21\u578b\u7684\u63a8\u7406\u4e0e\u5b66\u4e60\u80fd\u529b\uff1f<\/span><\/span><\/section>\n
    \u7cfb\u7edf\u5de5\u7a0b\uff1a\u5982\u4f55\u62e5\u62b1\u4e0d\u786e\u5b9a\u6027\uff0c\u5728\u5177\u4f53\u7684\u4e1a\u754c\u5b9e\u8df5\u4e2d\u521b\u9020\u4ef7\u503c\uff1f<\/span><\/span><\/section>\n

    <\/span><\/section>\n
    \u4e94\u4f4d\u53d1\u8d77\u4eba\u8001\u5e08\u4f1a\u5e26\u9886\u5927\u5bb6\u7814\u8bfb\u9886\u57df\u524d\u6cbf\u8bba\u6587\uff0c\u73b0\u8bda\u9080\u5bf9\u6b64\u8bdd\u9898\u611f\u5174\u8da3\u7684\u670b\u53cb\uff0c\u4e00\u8d77\u5171\u521b\u3001\u5171\u5efa\u3001\u5171\u4eab\u300c\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u300d\u4e3b\u9898\u793e\u533a\uff0c\u901a\u8fc7\u4e92\u76f8\u7684\u4ea4\u6d41\u4e0e\u78b0\u649e\uff0c\u4fc3\u8fdb\u6211\u4eec\u66f4\u6df1\u5165\u7684\u7406\u89e3\u4ee5\u4e0a\u95ee\u9898\u3002\u65e0\u8bba\u60a8\u662f\u81f4\u529b\u4e8e\u7a81\u7834AI\u53ef\u89e3\u91ca\u6027\u7406\u8bba\u74f6\u9888\u7684\u7814\u7a76\u8005\uff0c\u63a2\u7d22\u590d\u6742\u7cfb\u7edf\u4e0e\u667a\u80fd\u672c\u8d28\u7684\u4ea4\u53c9\u5b66\u79d1\u63a2\u7d22\u8005\uff0c\u8fd8\u662f\u8ffd\u6c42\u6a21\u578b\u5b89\u5168\u53ef\u4fe1\u7684\u5de5\u7a0b\u5b9e\u8df5\u8005\uff0c\u8bda\u9080\u60a8\u5171\u540c\u53c2\u4e0e\u8fd9\u573a\u63ed\u5f00\u5927\u6a21\u578b\u201c\u9ed1\u7bb1\u201d\u7684\u601d\u60f3\u76db\u5bb4\u3002<\/span><\/span><\/section>\n

    <\/span><\/section>\n
    \u8bfb\u4e66\u4f1a\u8ba1\u5212\u4e8e2025\u5e746\u670819\u65e5\u542f\u52a8\uff0c\u6bcf\u5468\u56db\u665a19:30-21:30\uff0c\u9884\u8ba1\u6301\u7eed\u5206\u4eab10\u5468\u5de6\u53f3\u3002<\/span><\/span><\/section>\n<\/section>\n
    <\/strong><\/strong><\/section>\n<\/section>\n
    \n
    <\/strong><\/strong><\/section>\n<\/section>\n
    \n
    <\/strong><\/strong><\/section>\n
    <\/strong><\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
    \n
    \n
    \n
    <\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
    \n
    \"\u56fe\u7247\"<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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

    \n
    \n
    \n
    \n
    \n

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

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

    \u80cc\u666f\u7b80\u4ecb<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

    \n

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

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


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

    \u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u81ea2012\u5e74\u4ee5\u6765\u53d6\u5f97\u4e86\u5de8\u5927\u7a81\u7834\uff0c\u7279\u522b\u662f\u4ee5\u5927\u6a21\u578b\u4e3a\u4ee3\u8868\u7684\u65b0\u4e00\u4ee3\u4eba\u5de5\u667a\u80fd\u6280\u672f\u3002\u4f46\u662f\uff0c\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u6a21\u5f0f\u7684\u5174\u8d77\uff0c\u201c\u7aef\u5230\u7aef\u201d\u7684\u5b66\u4e60\u6a21\u5f0f\u9010\u6e10\u5360\u636e\u4e3b\u5bfc\u5730\u4f4d\u3002\u7136\u800c\uff0c\u4eba\u4eec\u4e0d\u4e86\u89e3\u5b83\u662f\u5982\u4f55\u505a\u5230\u7684\uff0c\u53ea\u80fd\u89c6\u4e4b\u4e3a\u201c\u9ed1\u7bb1\u201d\u3002\u56e0\u6b64\uff0c\u4eba\u4eec\u5bf9AI\u7684\u6539\u8fdb\u88ab\u9650\u5236\u5728\u5806\u7b97\u529b\u4e0e\u6570\u636e\u4e0a\uff0c\u4e5f\u65e0\u6cd5\u5b8c\u5168\u4fe1\u4efbAI\u3002<\/span><\/span><\/p>\n


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

    <\/section>\n


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

    \u957f\u8fdc\u6765\u770b\uff0c\u57fa\u4e8e\u6570\u636e\u548c\u7b97\u529b\u8d44\u6e90Scaling law\u7684\u5927\u6a21\u578b\u80fd\u529b\u7684\u63d0\u5347\u4f1a\u8d70\u5230\u5c3d\u5934\u3002\u56e0\u6b64\uff0c\u6df1\u5ea6\u5b66\u4e60\u7684\u7814\u7a76\u8303\u5f0f\u9700\u8981\u4ece\u300c\u7ecf\u9a8c\u6027\u6280\u672f\u300d\u8f6c\u6362\u5230\u300c\u79d1\u5b66\u65b9\u6cd5\u8bba\u300d\u3002\u7406\u60f3\u60c5\u51b5\u4e0b\uff0c\u6700\u597d\u6211\u4eec\u80fd\u4ece\u7406\u8bba\u4e0a\u8bc1\u660e\u201c\u795e\u7ecf\u7f51\u7edc\u7684\u7cbe\u7ec6\u51b3\u7b56\u903b\u8f91\u201d\u53ef\u4ee5\u5728\u6570\u5b66\u4e0a\u88ab\u63a8\u5bfc\u548c\u5efa\u6a21\u3002\u5f53\u7136\uff0c\u4e0d\u5c11\u5b66\u8005\u4e5f\u4ece\u7edf\u8ba1\u7269\u7406\u3001\u590d\u6742\u79d1\u5b66\u7b49\u89c6\u89d2\u5c1d\u8bd5\u7406\u89e3\u5927\u6a21\u578b\u7684\u6d8c\u73b0\u673a\u5236\u3001\u4e0a\u4e0b\u6587\u5b66\u4e60\u80fd\u529b\u53ca\u7ed3\u6784\u4e0e\u529f\u80fd\u7684\u5bf9\u5e94\u5173\u7cfb\u7b49\u3002\u6700\u8fd1\uff0cAnthropic\u516c\u53f8\u4e5f\u5c1d\u8bd5\u62ff\u8d77\u300c\u624b\u672f\u5200\u300d\uff0c\u62c6\u89e3\u5927\u6a21\u578b\u7684\u5de5\u4f5c\u56de\u8def\uff0c\u5e0c\u671b\u80fd\u591f\u7406\u89e3\u5927\u6a21\u578b\u5728\u63a8\u7406\u3001\u521b\u4f5c\u3001\u7406\u89e3\u8bed\u8a00\u7b49\u5404\u79cd\u80fd\u529b\u4e0a\u7684\u5de5\u4f5c\u56de\u8def\u662f\u4ec0\u4e48\u6837\u7684\uff0c\u4e5f\u5f15\u8d77\u5927\u5bb6\u7684\u5e7f\u6cdb\u5173\u6ce8\u3002<\/span><\/span><\/p>\n


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

    <\/section>\n

    \u56fe\u7247\u6765\u6e90\uff1ahttps:\/\/www.anthropic.com\/research\/tracing-thoughts-language-model<\/span><\/span><\/p>\n


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

    \u4e3a\u4e86\u66f4\u6df1\u5165\u7684\u7406\u89e3\u5927\u8bed\u8a00\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\uff0c\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u526f\u6559\u6388\u5f20\u62f3\u77f3\u3001\u963f\u91cc\u4e91\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u56e2\u961f\u8d1f\u8d23\u4eba\u6c88\u65ed\u3001\u5f69\u4e91\u79d1\u6280\u9996\u5e2d\u79d1\u5b66\u5bb6\u8096\u8fbe\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7855\u58eb\u751f\u6768\u660e\u54f2\u548c\u6d59\u6c5f\u5927\u5b66\u535a\u58eb\u751f\u59da\u4e91\u5fd7\u5171\u540c\u53d1\u8d77\u300c\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u300d\u8bfb\u4e66\u4f1a<\/span><\/span>\u3002\u8bfb\u4e66\u4f1a<\/span><\/span>\u5c1d\u8bd5\u56de\u7b54\u4ee5\u4e0b\u95ee\u9898\uff1a<\/span><\/span><\/p>\n

      \n
    1. \n

      Transformer circuit\u6709\u6548\u7684\u6570\u5b66\u57fa\u7840\u662f\u4ec0\u4e48\uff1f<\/span><\/span><\/p>\n<\/li>\n

    2. \n

      \u5de5\u4f5c\u56de\u8def\u53d1\u73b0\u5982\u4f55\u8c03\u548c\u6548\u7387\u4e0e\u7c92\u5ea6\u7684\u95ee\u9898\uff1f<\/span><\/span><\/p>\n<\/li>\n

    3. \n

      \u5927\u8bed\u8a00\u6a21\u578b\u662f\u5982\u4f55\u8c03\u7528\u77e5\u8bc6\u5b8c\u6210\u4efb\u52a1\u7684\uff1f<\/span><\/span><\/p>\n<\/li>\n

    4. \n

      \u795e\u7ecf\u7f51\u7edc\u7684\u7cbe\u7ec6\u51b3\u7b56\u903b\u8f91\u662f\u5426\u53ef\u4ee5\u88ab\u4e25\u8c28\u5730\u89e3\u91ca\u6e05\u695a\uff1f<\/span><\/span><\/p>\n<\/li>\n

    5. \n

      \u51b3\u5b9a\u795e\u7ecf\u7f51\u7edc\u7684\u6027\u80fd\u7684\u6839\u56e0\u662f\u5426\u53ef\u4ee5\u88ab\u6e05\u6670\u7684\u6570\u5b66\u5efa\u6a21\uff1f<\/span><\/span><\/p>\n<\/li>\n

    6. \n

      \u6e17\u6d41<\/span><\/span><\/span>\u76f8\u53d8<\/span><\/span><\/span>\u3001<\/span><\/span><\/span>\u81ea\u7ec4\u7ec7<\/span><\/span><\/span>\u7406\u8bba<\/span><\/span><\/span>\u7b49<\/span><\/span><\/span>\u5982\u4f55\u89e3\u91ca\u5927\u8bed\u8a00\u6a21\u578b\u7684<\/span><\/span><\/span>\u6d8c\u73b0\u3001\u987f\u609f\u7b49\u73b0\u8c61<\/span><\/span><\/span>\uff1f<\/span><\/span><\/span><\/p>\n<\/li>\n

    7. \n

      \u7edf\u8ba1\u7269\u7406<\/span><\/span><\/span>\u6846\u67b6\u4e0b<\/span><\/span><\/span>\u5982\u4f55\u89e3\u91caIn-context learning \u7684\u673a\u7406\uff1f<\/span><\/span><\/span><\/p>\n<\/li>\n

    8. \n

      \u5982\u4f55\u4ece\u5927\u6a21\u578b\u4e2d\u8bc6\u522b\u4e0d\u540c\u6a21\u5757\u7684\u91cd\u8981\u6027\uff1f<\/span><\/span><\/p>\n<\/li>\n

    9. \n

      \u5982\u4f55\u57fa\u4e8e\u6709\u9650\u7684\u5185\u90e8\u51b3\u7b56\u8def\u5f84\u63d0\u5347\u6a21\u578b\u6027\u80fd\u4e0e\u5b89\u5168\u6027\uff1f<\/span><\/span><\/p>\n<\/li>\n

    10. \n

      \u5982\u4f55\u5c06\u5206\u6790\u63a8\u7406\u6a21\u578b\u4e0e\u4e00\u822c\u6a21\u578b\u878d\u5408\uff1f<\/span><\/span><\/p>\n<\/li>\n<\/ol>\n


      <\/span><\/h2>\n


      <\/span><\/h2>\n

      \n
      \n
      \n
      \n
      \n

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

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

      \u8bfb\u4e66\u4f1a\u6846\u67b6<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

      \n

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

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


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

      \u8bfb\u4e66\u4f1a\u5c06\u56f4\u7ed5\u4ee5\u4e0b\u6a21\u5757\u5c55\u5f00\uff1a<\/span><\/span><\/p>\n

        \n
      1. \n

        \u81ea\u4e0b\u800c\u4e0a\u7684\u5de5\u4f5c\u56de\u8def\u89c6\u89d2\uff1a\u4ece\u8fd8\u539f\u8bba\u7684\u89c6\u89d2\u7406\u89e3\u5927\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\u673a\u5236\uff0c\u7ed8\u5236\u5927\u6a21\u578b\u7684\u5de5\u4f5c\u56de\u8def\uff0c\u5e76\u53cd\u8fc7\u6765\u7406\u89e3\u5927\u6a21\u578b\u662f\u600e\u4e48\u5de5\u4f5c\u7684\u3002\u6211\u4eec\u5c06\u7cfb\u7edf\u68b3\u7406\u5305\u62ec\u5927\u8bed\u8a00\u6a21\u578b\u67b6\u6784\u4e2d\u5de5\u4f5c\u56de\u8def\u76f8\u5173\u7684\u7814\u7a76\u5de5\u4f5c\uff0c\u91cd\u70b9\u5173\u6ce8\uff1a1) Transformer circuits \u6570\u5b66\u57fa\u7840\uff1b2\uff09\u56de\u8def\u8bc6\u522b\u4e0e\u7a00\u758f\u5316\u65b9\u6cd5\uff1b<\/span><\/span><\/p>\n<\/li>\n

      2. \n

        \u81ea\u4e0a\u800c\u4e0b\u7684\u7b49\u6548\u4ea4\u4e92\u89c6\u89d2\uff1a\u4ece\u7406\u8bba\u5c42\u9762\u63a8\u5bfc\u795e\u7ecf\u7f51\u7edc\u7684\u7cbe\u7ec6\u51b3\u7b56\u903b\u8f91\uff0c\u89e3\u91ca\u6e05\u695a\u51b3\u5b9a\u795e\u7ecf\u7f51\u7edc\u7684\u6027\u80fd\u7684\u6839\u56e0\uff0c\u5c06\u7ecf\u9a8c\u6027\u8c03\u4f18\u8f6c\u5316\u4e3a\u53ef\u8bc1\u660e\u7684\u7406\u8bba\u4f53\u7cfb\uff0c\u7a81\u7834Scaling law\u7684\u684e\u688f\uff1b<\/span><\/span><\/p>\n<\/li>\n

      3. \n

        \u590d\u6742\u79d1\u5b66\u89c6\u89d2\uff1a\u4ece\u590d\u6742\u79d1\u5b66\u3001\u7edf\u8ba1\u7269\u7406\u4e2d\u7684\u6d8c\u73b0\u3001\u81ea\u7ec4\u7ec7\u3001\u5206\u5f62\u3001\u6e17\u6d41\u76f8\u53d8\u7b49\u7406\u8bba\u89c6\u89d2\uff0c\u5c06\u5927\u6a21\u578b\u770b\u4f5c\u4e00\u4e2a\u590d\u6742\u7cfb\u7edf\uff0c\u8bd5\u56fe\u7406\u89e3\u5176\u5728\u6267\u884c\u4efb\u52a1\u7684\u8fc7\u7a0b\u4e2d\u8868\u73b0\u51fa\u7684Scaling law\uff0cGroking\u548cIn context learning\u7b49\u80fd\u529b\uff1b<\/span><\/span><\/p>\n<\/li>\n

      4. \n

        \u7cfb\u7edf\u5de5\u7a0b\u89c6\u89d2\uff1a\u805a\u7126\u6a21\u578b\u7684\u57fa\u7840\u7b97\u6cd5\u6a21\u5757\u3001\u5185\u90e8\u51b3\u7b56\u8def\u5f84\u53ca\u7aef\u5230\u7aef\u5904\u7406\u903b\u8f91\u3002\u901a\u8fc7\u89e3\u6790\u5927\u6a21\u578b\u7684\u539f\u5b50\u7b97\u6cd5\uff08\u5982\u4efb\u52a1\u5b9a\u4f4d\u3001\u6a21\u578b\u89e3\u91ca\u3001\u8c03\u4f18\u7b56\u7565\uff09\uff0c\u8fd8\u539f\u5176\u4ece\u8f93\u5165\u5230\u8f93\u51fa\u7684\u6bcf\u4e00\u6b65 \u201c\u601d\u8003\u201d \u8fc7\u7a0b\uff0c\u5b9e\u73b0\u4ece\u7b97\u6cd5\u5c42\u9762\u7684\u900f\u660e\u5316\uff0c\u4ece\u800c\u5b9e\u73b0\u5bf9\u5927\u6a21\u578b\u6027\u80fd\u4e0e\u5b89\u5168\u6027\u7684\u6709\u6548\u63d0\u5347\u3002<\/span><\/span><\/p>\n<\/li>\n<\/ol>\n


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


        <\/span><\/h2>\n


        <\/span><\/h2>\n

        \n
        \n
        \n
        \n
        \n

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

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

        \u53d1\u8d77\u4eba\u56e2\u961f<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

        \n

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

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


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

        <\/section>\n

        \u5f20\u62f3\u77f3\uff0c\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u526f\u6559\u6388\uff0c\u535a\u58eb\u751f\u5bfc\u5e08\uff0c\u5165\u9009\u56fd\u5bb6\u7ea7\u6d77\u5916\u9ad8\u5c42\u6b21\u4eba\u624d\u5f15\u8fdb\u8ba1\u5212\uff0c\u83b7ACM China\u65b0\u661f\u5956\u30022014\u5e74\u83b7\u5f97\u65e5\u672c\u4e1c\u4eac\u5927\u5b66\u535a\u58eb\u5b66\u4f4d\uff0c2014\u20142018\u5e74\u5728\u52a0\u5dde\u5927\u5b66\u6d1b\u6749\u77f6\u5206\u6821\uff08UCLA\uff09\u4ece\u4e8b\u535a\u58eb\u540e\u7814\u7a76\uff0c\u5408\u4f5c\u5bfc\u5e08\u4e3a\u6731\u677e\u7eaf\u6559\u6388\u3002\u5728\u795e\u7ecf\u7f51\u7edc\u53ef\u89e3\u91ca\u6027\u65b9\u5411\u53d6\u5f97\u4e86\u591a\u9879\u5177\u6709\u56fd\u9645\u5f71\u54cd\u529b\u7684\u6210\u679c\uff0c\u5176\u7814\u7a76\u6210\u679c\u53d1\u8868\u5728IEEE T-PAMI\u3001ICML\u3001ICLR\u3001CVPR\u7b49\u9876\u7ea7\u671f\u520a\u548c\u4f1a\u8bae\u4e0a\uff0c\u5e76\u62c5\u4efbIJCAI\u53ef\u89e3\u91ca\u6027\u65b9\u5411Tutorial\u4e3b\u8bb2\u4eba\u53caAAAI\u3001CVPR\u3001ICML\u7b49\u4f1a\u8bae\u7684\u5206\u8bba\u575b\u4e3b\u5e2d\u3002<\/span><\/span><\/p>\n

        \u7814\u7a76\u65b9\u5411\uff1a<\/span><\/span>\u53ef\u89e3\u91ca\u6027\u673a\u5668\u5b66\u4e60\uff0c<\/span><\/span>\u63d0\u51fa\u7b49\u6548\u4ea4\u4e92\u53ef\u89e3\u91ca\u6027\u7406\u8bba\u4f53\u7cfb\u3002<\/span><\/span><\/p>\n


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

        <\/section>\n

        \u6c88\u65ed\uff0c\u963f\u91cc\u4e91-\u98de\u5929\u5b9e\u9a8c\u5ba4\u9ad8\u7ea7\u7b97\u6cd5\u4e13\u5bb6\uff0c\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u8d1f\u8d23\u4eba\u3002\u535a\u58eb\u6bd5\u4e1a\u4e8e\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66\u3002\u66fe\u83b7\u6d59\u6c5f\u7701\u79d1\u6280\u8fdb\u6b65\u4e00\u7b49\u5956\uff0c\u5728ICLR\/ICML\/NeurIPS\/ACL\/CVPR\/ECCV\/ICCV\u7b49\u56fd\u9645\u9876\u4f1a\u4e0a\u53d1\u8868\u8bba\u658740\u4f59\u7bc7\uff0cgoogle scholar\u5f15\u75281900\u4f59\u6b21\u3002<\/span><\/span><\/p>\n

        \u7814\u7a76\u65b9\u5411\uff1a\u805a\u7126\u5927\u6a21\u578b\u7684\u5185\u5728\u673a\u7406\u548c\u8fd0\u884c\u673a\u5236\uff0c\u91cd\u70b9\u63a2\u7d22\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u7b49\u6838\u5fc3\u95ee\u9898\uff0c\u5e76\u81f4\u529b\u4e8e\u5c06\u5927\u8bed\u8a00\u6a21\u578b\u6280\u672f\u5e94\u7528\u5728\u56fd\u9645\u5927\u578b\u8d5b\u4e8b\u3001\u4e3b\u6743\u5927\u6a21\u578b\u7b49\u5173\u952e\u573a\u666f\u3002<\/span><\/span><\/p>\n


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

        <\/section>\n

        \u8096\u8fbe\uff0c\u4eba\u5de5\u667a\u80fd\u516c\u53f8\u5f69\u4e91\u79d1\u6280\u8054\u5408\u521b\u59cb\u4eba\u3001\u9996\u5e2d\u79d1\u5b66\u5bb6\u3001\u5317\u4eac\u90ae\u7535\u5927\u5b66\u7f51\u7edc\u7a7a\u95f4\u5b89\u5168\u5b66\u9662\u8bb2\u5e08\u3002<\/span><\/span><\/p>\n

        \u7814\u7a76\u65b9\u5411\uff1a\u4e3b\u8981\u8d1f\u8d23\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u548c\u7b97\u6cd5\u7684\u7814\u53d1\u7528\u4e8e\u5f69\u4e91\u5929\u6c14\u3001\u5f69\u4e91\u5c0f\u8bd1\u3001\u5f69\u4e91\u5c0f\u68a6\u7b49\u4ea7\u54c1\u3002<\/span><\/span><\/p>\n


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

        <\/section>\n

        \u6768\u660e\u54f2\uff0c\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7cfb\u7edf\u79d1\u5b66\u5b66\u9662\u7855\u58eb\u751f\uff0c\u5f20\u6c5f\u8001\u5e08\u56e0\u679c\u6d8c\u73b0\u7814\u7a76\u5c0f\u7ec4\u6210\u5458\u3002<\/span><\/span><\/p>\n

        \u7814\u7a76\u65b9\u5411\uff1a\u56e0\u679c\u6d8c\u73b0\u3001\u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u3002<\/span><\/span><\/p>\n


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

        <\/section>\n

        \u59da\u4e91\u5fd7\uff0c\u6d59\u6c5f\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u4e0e\u6280\u672f\u5b66\u9662\u5728\u8bfb\u535a\u58eb\u751f\uff0c\u5bfc\u5e08\u4e3a\u9648\u534e\u94a7\u6559\u6388\u4e0e\u5f20\u5b81\u8c6b\u6559\u6388\u3002\u73b0\u5728\u662f\u52a0\u5dde\u5927\u5b66\u6d1b\u6749\u77f6\u5206\u6821\u7684\u8bbf\u95ee\u7814\u7a76\u5b66\u8005\uff0c\u4e0eNanyun Peng\u6559\u6388\u4e00\u8d77\u5de5\u4f5c\u3002<\/span><\/span><\/p>\n

        \u7814\u7a76\u65b9\u5411\uff1a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7684\u673a\u5668\u5b66\u4e60\uff0c\u7279\u522b\u5173\u6ce8\u652f\u6491\u5927\u578b\u8bed\u8a00\u6a21\u578b \uff08LLM\uff09 \u7684\u77e5\u8bc6\u673a\u5236\u3002\u7814\u7a76 LLM \u5982\u4f55\u83b7\u53d6\u3001\u5b58\u50a8\u548c\u5229\u7528\u77e5\u8bc6\u8fdb\u884c\u63a8\u7406\uff0c\u4ee5\u53ca\u4e0d\u540c\u67b6\u6784\u548c\u6a21\u5f0f\u7684\u6a21\u578b\u4e4b\u95f4\u7684\u4ea4\u4e92\u65b9\u5f0f\u3002\u76ee\u6807\u662f\u5f00\u53d1\u7b80\u6d01\u800c\u7cbe\u786e\u7684\u6a21\u578b\u7f16\u8f91\u65b9\u6cd5\u3002<\/span><\/span><\/p>\n


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


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

        \n
        \n
        \n
        \n
        \n

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

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

        \u62a5\u540d\u53c2\u4e0e\u8bfb\u4e66\u4f1a<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

        \n

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

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


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

        \u8fd0\u884c\u6a21\u5f0f<\/span><\/span><\/strong><\/span><\/h2>\n

        \u4ece2025\u5e746\u670819\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u56db\u665a 19:30-21:30<\/span><\/span><\/strong>\uff0c\u6301\u7eed\u65f6\u95f4\u9884\u8ba110\u5468\u5de6\u53f3\uff0c\u6309\u8bfb\u4e66\u4f1a\u6846\u67b6\u8bbe\u8ba1\uff0c\u6bcf\u5468\u8fdb\u884c\u7ebf\u4e0a\u4f1a\u8bae\uff0c\u4e0e\u4e3b\u8bb2\u4eba\u7b49\u793e\u533a\u6210\u5458\u5f53\u9762\u4ea4\u6d41\uff0c\u4f1a\u540e\u53ef\u4ee5\u83b7\u5f97\u89c6\u9891\u56de\u653e\u6301\u7eed\u5b66\u4e60\u3002<\/span><\/span><\/p>\n


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

        \u62a5\u540d\u65b9\u5f0f<\/span><\/span><\/strong><\/span><\/h2>\n

        \u7b2c\u4e00\u6b65\uff1a\u5fae\u4fe1\u626b\u7801\u586b\u5199\u62a5\u540d\u4fe1\u606f\u3002<\/span><\/span><\/p>\n

        <\/section>\n

        \u626b\u7801\u62a5\u540d\uff08\u53ef\u5f00\u53d1\u7968\uff09<\/span><\/span><\/span><\/p>\n

        \u7b2c\u4e8c\u6b65\uff1a\u586b\u5199\u4fe1\u606f\u540e\uff0c\u4ed8\u8d39\u62a5\u540d\u3002\u5982\u9700\u7528\u652f\u4ed8\u5b9d\u652f\u4ed8\uff0c\u8bf7\u5728PC\u7aef\u8fdb\u5165\u8bfb\u4e66\u4f1a\u9875\u9762\u62a5\u540d\u652f\u4ed8\uff1a<\/span><\/span><\/p>\n

        https:\/\/pattern.swarma.org\/study_group\/63<\/span><\/span><\/p>\n

        \u7b2c\u4e09\u6b65\uff1a\u6dfb\u52a0\u8fd0\u8425\u8d1f\u8d23\u4eba\u5fae\u4fe1\uff0c\u62c9\u5165\u5bf9\u5e94\u4e3b\u9898\u7684\u8bfb\u4e66\u4f1a\u793e\u533a\uff08\u5fae\u4fe1\u7fa4\uff09\u3002<\/span><\/span><\/p>\n


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

        PS\uff1a\u4e3a\u786e\u4fdd\u4e13\u4e1a\u6027\u548c\u8ba8\u8bba\u7684\u805a\u7126\uff0c\u672c\u8bfb\u4e66\u4f1a\u8c22\u7edd\u8131\u79bb\u8bfb\u4e66\u4f1a\u4e3b\u9898\u548c\u590d\u6742\u79d1\u5b66\u95ee\u9898\u672c\u8eab\u7684\u7a7a\u6cdb\u7684\u54f2\u5b66\u548c\u601d\u8fa8\u5f0f\u8ba8\u8bba\uff1b\u5982\u679c\u51fa\u73b0\u8ba8\u8bba\u5185\u5bb9\u4e0d\u7b26\u5408\u8981\u6c42\u3001\u7ecf\u63d0\u9192\u65e0\u6548\u8005\uff0c\u4f1a\u88ab\u79fb\u9664\u7fa4\u804a\u5e76\u5bf9\u672a\u53c2\u4e0e\u90e8\u5206\u9000\u8d39\u3002<\/span><\/span><\/em><\/p>\n


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

        \u52a0\u5165\u793e\u533a\u540e\u53ef\u4ee5\u83b7\u5f97\u7684\u8d44\u6e90\uff1a<\/span><\/span><\/strong><\/span><\/h3>\n

        \u5b8c\u6574\u6743\u9650\uff0c\u5305\u62ec<\/span><\/span>\u7ebf\u4e0a\u95ee\u7b54\u3001\u5f55\u64ad\u56de\u770b\u3001\u8d44\u6599\u5171\u4eab\u3001\u793e\u7fa4\u4ea4\u6d41\u3001\u4fe1\u606f\u540c\u6b65\u3001\u5171\u521b\u4efb\u52a1\u83b7\u53d6\u79ef\u5206\u7b49<\/span><\/span><\/strong>\u3002<\/span><\/span><\/p>\n

        <\/section>\n


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

        \u53c2\u4e0e\u5171\u521b\u4efb\u52a1\u83b7\u53d6\u79ef\u5206\uff0c\u5171\u5efa\u5b66\u672f\u793e\u533a\uff1a<\/span><\/span><\/span><\/h3>\n

        \u8bfb\u4e66\u4f1a\u91c7\u7528\u5171\u5b66\u5171\u7814\u673a\u5236\uff0c\u6210\u5458\u901a\u8fc7\u5185\u5bb9\u5171\u521b\u83b7\u79ef\u5206\uff08<\/span><\/span>\u5b57\u5e55\u4fee\u6539\u3001\u8bfb\u4e66\u4f1a\u7b14\u8bb0\u3001\u8bba\u6587\u901f\u9012\u3001\u516c\u4f17\u53f7\u6587\u7ae0\u3001\u96c6\u667a\u767e\u79d1\u3001\u8bba\u6587\u89e3\u8bfb\u7b49\u5171\u521b\u4efb\u52a1<\/span><\/span><\/strong>\uff09\uff0c\u79ef\u5206\u7b26\u5408\u6761\u4ef6\u5373\u53ef\u9000\u8d39\u3002\u53d1\u8d77\u4eba\u548c\u4e3b\u8bb2\u4eba\u540c\u6837\u9075\u5faa\u6b64\u673a\u5236\uff0c\u65e0\u989d\u5916\u91d1\u94b1\u6fc0\u52b1\u3002<\/span><\/span><\/p>\n

        <\/section>\n

        PS\uff1a\u5177\u4f53\u53c2\u4e0e\u65b9\u5f0f\u53ef\u4ee5\u52a0\u5165\u8bfb\u4e66\u4f1a\u540e\u67e5\u770b\u5bf9\u5e94\u7684\u5171\u521b\u4efb\u52a1\u5217\u8868\uff0c\u9886\u53d6\u4efb\u52a1\uff0c\u4e0e\u8fd0\u8425\u8d1f\u8d23\u4eba\u6c9f\u901a\u8be6\u60c5\uff0c\u4e0a\u8ff0\u89c4\u5219\u7684\u6700\u7ec8\u89e3\u91ca\u6743\u5f52\u96c6\u667a\u4ff1\u4e50\u90e8\u6240\u6709\u3002<\/span><\/span><\/p>\n


        <\/span><\/h2>\n


        <\/span><\/h2>\n

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

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

        \u8bfb\u4e66\u4f1a\u9605\u8bfb\u6750\u6599<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

        \n

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

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


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

        \u9605\u8bfb\u6750\u6599\u8f83\u957f\uff0c\u4e3a\u4e86\u66f4\u597d\u7684\u9605\u8bfb\u4f53\u9a8c\uff0c\u5efa\u8bae\u60a8\u524d\u5f80\u96c6\u667a\u6591\u56fe\u6c89\u6d78\u5f0f\u9605\u8bfb\uff0c\u5e76\u53ef\u6536\u85cf\u611f\u5174\u8da3\u7684\u8bba\u6587\u3002<\/span><\/span><\/p>\n

        <\/section>\n

        https:\/\/pattern.swarma.org\/article\/348?from=wechat<\/span><\/span><\/p>\n

        \u8bfb\u4e66\u4f1a\u9605\u8bfb\u6e05\u5355<\/span><\/span><\/p>\n


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

        \u81ea\u4e0b\u800c\u4e0a\uff1a\u5de5\u4f5c\u56de\u8def\u89c6\u89d2<\/span><\/span><\/h3>\n
        <\/section>\n

        \u56fe\u7247\u6765\u6e90\uff1ahttps:\/\/www.anthropic.com\/news\uff08Tracing the thoughts of a large language model\uff09<\/span><\/span><\/p>\n


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

        \u6570\u5b66\u57fa\u7840<\/span><\/span><\/h4>\n

        1.\u63d0\u51fa\u4e00\u79cd\u6570\u5b66\u6846\u67b6\uff0c\u5c06Transformer\u6a21\u578b\uff08\u5982Claude\uff09\u7684\u8ba1\u7b97\u8fc7\u7a0b\u5206\u89e3\u4e3a\u53ef\u89e3\u91ca\u7684\u7535\u8def\u5143\u4ef6\uff08\u5982\u6ce8\u610f\u529b\u5934\u3001\u524d\u9988\u7f51\u7edc\u5c42\uff09\uff0c\u901a\u8fc7\u7ebf\u6027\u4ee3\u6570\u5de5\u5177\u91cf\u5316\u5404\u5143\u4ef6\u5bf9\u8f93\u51fa\u7684\u8d21\u732e\u3002<\/span><\/span><\/p>\n

        Elhage, Nelson, et al. “A mathematical framework for transformer circuits.” <\/span><\/span>Transformer Circuits Thread<\/span><\/span><\/em> 1.1 (2021): 12.<\/span><\/span><\/p>\n


        <\/span><\/h4>\n

        \u56de\u8def\u53d1\u73b0<\/span><\/span><\/h4>\n

        1.\u7c7b\u6bd4\u751f\u7269\u7cfb\u7edf\u7684\u8fdb\u5316\u4e0e\u9002\u5e94\u6027\uff0c\u5206\u6790\u5927\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u4e2d\u795e\u7ecf\u7ed3\u6784\u7684\u201c\u529f\u80fd\u5206\u5316\u201d\u73b0\u8c61\u3002<\/span><\/span><\/p>\n

        Lindsey, Jack, et al. “On the biology of a large language model.” <\/span><\/span>Transformer Circuits Thread<\/span><\/span><\/em> (2025).<\/span><\/span><\/p>\n

        2.\u5f00\u53d1\u81ea\u52a8\u5316\u7535\u8def\u8ffd\u8e2a\u6280\u672f\uff0c\u901a\u8fc7\u5e72\u9884\u5b9e\u9a8c\u548c\u68af\u5ea6\u5206\u6790\uff0c\u7ed8\u5236LLM\u5185\u90e8\u7279\u5b9a\u4efb\u52a1\uff08\u5982\u95ee\u7b54\u3001\u903b\u8f91\u63a8\u7406\uff09\u7684\u52a8\u6001\u8ba1\u7b97\u5b50\u56fe\u3002<\/span><\/span><\/p>\n

        Ameisen, Emmanuel, et al. “Circuit tracing: Revealing computational graphs in language models.” <\/span><\/span>Transformer Circuits Thread<\/span><\/span><\/em> (2025).<\/span><\/span><\/p>\n

        3.\u8be5\u8bba\u6587\u901a\u8fc7\u9006\u5411\u5de5\u7a0b\u65b9\u6cd5\uff0c\u5728GPT-2 small\u6a21\u578b\u4e2d\u8bc6\u522b\u51fa\u4e00\u4e2a\u4e13\u95e8\u8d1f\u8d23\u95f4\u63a5\u5bbe\u8bed\u8bc6\u522b\u7684\u795e\u7ecf\u56de\u8def\uff0c\u63ed\u793a\u4e86\u8bed\u8a00\u6a21\u578b\u5904\u7406\u8bed\u6cd5\u5173\u7cfb\u7684\u6a21\u5757\u5316\u673a\u5236\uff0c\u4f46\u6548\u7387\u8f83\u4f4e\u3002<\/span><\/span><\/p>\n

        Wang, Kevin, et al. “Interpretability in the wild: a circuit for indirect object identification in gpt-2 small.” <\/span><\/span>arXiv preprint arXiv:2211.00593<\/span><\/span><\/em> (2022).<\/span><\/span><\/p>\n

        4.\u6548\u7387\u66f4\u9ad8\u7684\u56de\u8def\u53d1\u73b0\u65b9\u6cd5\uff0c\u628atransformer\u770b\u4f5c\u56fe\uff0c\u901a\u8fc7\u8ba1\u7b97\u68af\u5ea6\u8bc4\u4f30\u56fe\u4e2d\u8fb9\u7684\u91cd\u8981\u6027\u8f6c\u5316\u4e3a\u7a00\u758f\u56fe\uff0c\u540c\u6837\u4e5f\u80fd\u505a\u4efb\u52a1<\/span><\/span><\/p>\n

        Hanna, Michael, Sandro Pezzelle, and Yonatan Belinkov. “Have faith in faithfulness: Going beyond circuit overlap when finding model mechanisms.” <\/span><\/span>arXiv preprint arXiv:2403.17806<\/span><\/span><\/em> (2024).<\/span><\/span><\/p>\n

        5.\u56de\u8def\u53d1\u73b0\u65b9\u6cd5\uff0c\u4e0eAnthropic\u76f8\u5173\uff0c\u5bf9\u4e8e\u56de\u8def\u7684\u53ef\u89e3\u91ca\u6027\u66f4\u9ad8\uff08\u7c92\u5ea6\u66f4\u7ec6\uff09\uff0c\u5728\u539f\u59cb\u6a21\u578b\u4e0a\u5728\u8bad\u7ec3\u4e00\u4e2a\u6a21\u578b\u89e3\u51b3\u6548\u7387\u7684\u95ee\u9898<\/span><\/span><\/p>\n

        Dunefsky, Jacob, Philippe Chlenski, and Neel Nanda. “Transcoders find interpretable llm feature circuits.” <\/span><\/span>arXiv preprint arXiv:2406.11944<\/span><\/span><\/em> (2024)<\/span><\/span><\/p>\n


        <\/span><\/h4>\n

        \u77e5\u8bc6\u8c03\u7528<\/span><\/span><\/h4>\n

        1.\u63ed\u793a\u4e86Transformer\u6a21\u578b\u901a\u8fc7\u7279\u5b9a\u6ce8\u610f\u529b\u5934\u548c\u524d\u9988\u7f51\u7edc\u7684\u52a8\u6001\u534f\u4f5c\u673a\u5236\u5b9e\u73b0\u4e8b\u5b9e\u77e5\u8bc6\u68c0\u7d22\uff0c\u63d0\u51fa\u77e5\u8bc6\u4ee5\u952e\u503c\u5bf9\u5f62\u5f0f\u5b58\u50a8\u4e8eFFN\u4e2d\u5e76\u901a\u8fc7\u6ce8\u610f\u529b\u5339\u914d\u6fc0\u6d3b\u7684\u6838\u5fc3\u89c2\u70b9\u3002<\/span><\/span><\/p>\n

        Lv, Ang, et al. “Interpreting key mechanisms of factual recall in transformer-based language models.” <\/span><\/span>arXiv preprint arXiv:2403.19521<\/span><\/span><\/em> (2024).<\/span><\/span><\/p>\n

        2.\u5b9a\u4e49\u4e86\u9884\u8bad\u7ec3Transformer\u4e2d\u7a00\u758f\u4e14\u6a21\u5757\u5316\u7684”\u77e5\u8bc6\u56de\u8def”\u7ed3\u6784\uff0c\u8bc1\u660e\u4e0d\u540c\u4e8b\u5b9e\u4f9d\u8d56\u7279\u5b9a\u5b50\u7f51\u7edc\u8def\u5f84\uff0c\u5e76\u53ef\u901a\u8fc7\u7f16\u8f91\u7535\u8def\u53c2\u6570\u5b9e\u73b0\u77e5\u8bc6\u7684\u5b9a\u5411\u4fee\u6539\u3002<\/span><\/span><\/p>\n

        Yao, Yunzhi, et al. “Knowledge circuits in pretrained transformers.” <\/span><\/span>arXiv preprint arXiv:2405.17969<\/span><\/span><\/em> (2024).<\/span><\/span><\/p>\n


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

        \u81ea\u4e0a\u800c\u4e0b\uff1a\u7b49\u6548\u4ea4\u4e92\u89c6\u89d2<\/span><\/span><\/h3>\n
        <\/section>\n

        \u4ec0\u4e48\u624d\u662f\u89e3\u91ca\u6027\u9886\u57df\u7684\u7b2c\u4e00\u6027\u539f\u7406\uff1f\u5f20\u62f3\u77f3\u56e2\u961f\u8ba4\u4e3a\u9700\u8981\u5728\u4e00\u4e2a\u65b0\u7684\u7406\u8bba\u4f53\u7cfb\u4e2d\uff0c\u63d0\u51fa\u5927\u91cf\u516c\u7406\u6027\u8981\u6c42\uff0c\u5f97\u51fa\u4e00\u4e2a\u53ef\u4ee5\u7cbe\u786e\u3001\u4e25\u8c28\u89e3\u91ca\u795e\u7ecf\u7f51\u7edc\u5185\u5728\u673a\u7406\u7684\u7406\u8bba\u624d\u53eb\u7b2c\u4e00\u6027\u539f\u7406\u3002\u4ed6\u4eec\u4ece0\u52301\u642d\u5efa\u4e86\u300c\u7b49\u6548\u4ea4\u4e92\u53ef\u89e3\u91ca\u6027\u7406\u8bba\u4f53\u7cfb\u300d\uff0c\u4ece\u4e09\u4e2a\u89d2\u5ea6\u6765\u89e3\u91ca\u795e\u7ecf\u7f51\u7edc\u7684\u5185\u5728\u673a\u7406\u3002<\/span><\/span><\/p>\n

        \u9996\u5148\u662f\u8bed\u4e49\u89e3\u91ca\u7684\u7406\u8bba\u57fa\u7840\u3002\u5f20\u62f3\u77f3\u56e2\u961f\u627e\u5230\u4e86\u5728\u5927\u90e8\u5206\u5e94\u7528\u4e2d\u795e\u7ecf\u7f51\u7edc\u90fd\u53ef\u4ee5\u6ee1\u8db3\u7684\u9762\u5411\u906e\u6321\u9c81\u68d2\u6027\u7684\u4e09\u4e2a\u5e38\u89c1\u6761\u4ef6\uff0c\u5e76\u4e14\u6570\u5b66\u8bc1\u660e\u4e86\u6ee1\u8db3\u8fd9\u4e09\u4e2a\u6761\u4ef6\u7684\u795e\u7ecf\u7f51\u7edc\u51b3\u7b56\u903b\u8f91\u53ef\u4ee5\u88ab\u5199\u6210\u7b26\u53f7\u5316\u7684\u4ea4\u4e92\u6982\u5ff5\u3002<\/span><\/span><\/p>\n

        1.\u8bc1\u660e\u795e\u7ecf\u7f51\u7edc\u7cbe\u7ec6\u51b3\u7b56\u903b\u8f91\u53ef\u4ee5\u4e25\u683c\u89e3\u91ca\u4e3a\u7b26\u53f7\u5316\u7b49\u6548\u4ea4\u4e92\u6982\u5ff5<\/span><\/span><\/p>\n

        Qihan Ren, Jiayang Gao, Wen Shen, and Quanshi Zhang. Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs. In ICLR, 2024.<\/span><\/span><\/p>\n

        https:\/\/zhuanlan.zhihu.com\/p\/693747946<\/span><\/span><\/p>\n

        \u5176\u6b21\u662f\u5bfb\u627e\u6027\u80fd\u80cc\u540e\u53ef\u8bc1\u660e\u3001\u53ef\u9a8c\u8bc1\u7684\u6839\u56e0\u3002\u5c06\u795e\u7ecf\u7f51\u7edc\u6cdb\u5316\u6027\u548c\u9c81\u68d2\u6027\u7b49\u7ec8\u6781\u6027\u80fd\u6027\u80fd\u6307\u6807\u62c6\u5206\u6210\u5177\u4f53\u5c11\u6570\u7ec6\u8282\u903b\u8f91\u3002\u4ed6\u4eec\u8bc1\u660e\uff1a\u7b49\u6548\u4ea4\u4e92\u7684\u590d\u6742\u5ea6\u53ef\u4ee5\u76f4\u63a5\u51b3\u5b9a\u795e\u7ecf\u7f51\u7edc\u7684\u9c81\u68d2\u6027\/\u8fc1\u79fb\u6027\uff0c\u51b3\u5b9a\u795e\u7ecf\u7f51\u7edc\u7684\u8868\u5f81\u80fd\u529b\u3002<\/span><\/span><\/p>\n

        2.\u4ece\u795e\u519c\u5c1d\u767e\u8349\u5230\u7cbe\u70bc\u4e0e\u8403\u53d6\uff1a\u8bba\u795e\u7ecf\u7f51\u7edc\u5bf9\u6297\u8fc1\u79fb\u6027<\/span><\/span><\/p>\n

        Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang, \u201cA Unified Approach to Interpreting and Boosting Adversarial Transferability\u201d in ICLR 2021<\/span><\/span><\/p>\n

        https:\/\/zhuanlan.zhihu.com\/p\/369883667<\/span><\/span><\/p>\n

        3.\u795e\u7ecf\u7f51\u7edc\u7684\u535a\u5f08\u4ea4\u4e92\u89e3\u91ca\u6027\uff08\u516d\uff09\uff1a\u4ece\u535a\u5f08\u4ea4\u4e92\u5c42\u9762\u89e3\u91ca\u5bf9\u6297\u9c81\u68d2\u6027<\/span><\/span><\/p>\n

        Jie Ren*, Die Zhang*, Yisen Wang*, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, and Quanshi Zhang (Correspondence), \u201cA Unified Game-Theoretic Interpretation of Adversarial Robustness\u201d in Neurips 2021<\/span><\/span><\/p>\n

        https:\/\/zhuanlan.zhihu.com\/p\/361686461<\/span><\/span><\/p>\n

        4.\u53ef\u89e3\u91ca\u6027\uff1a\u795e\u7ecf\u7f51\u7edc\u5bf9\u4ea4\u4e92\u6982\u5ff5\u8868\u8fbe\u80fd\u529b\u7684\u89e3\u6790\u5206\u6790<\/span><\/span><\/p>\n

        Dongrui Liu, Huiqi Deng, Xu Cheng, Qihan Ren, Kangrui Wang, and Quanshi Zhang, “Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities” in NeurIPS 2023<\/span><\/span><\/p>\n

        https:\/\/zhuanlan.zhihu.com\/p\/704760363<\/span><\/span><\/p>\n

        5.\u53d1\u73b0\u5e76\u8bc1\u660e\u795e\u7ecf\u7f51\u7edc\u8868\u5f81\u74f6\u9888<\/span><\/span><\/p>\n

        Discovering and Explaining the Representation Bottleneck of DNNs\uff0cHuiqi Deng*, Qihan Ren*, Hao Zhang, andQuanshi Zhang (Correspondence), ICLR (Oral), 2022<\/span><\/span><\/p>\n

        https:\/\/zhuanlan.zhihu.com\/p\/468569001<\/span><\/span><\/p>\n

        \u6700\u540e\u9700\u8981\u5bf9\u6df1\u5ea6\u5b66\u4e60\u7b97\u6cd5\u5728\u5de5\u7a0b\u4e0a\u7edf\u4e00\u3002\u4ed6\u4eec\u8bc1\u660e\u4e8614\u79cd\u4e0d\u540c\u7684\u8f93\u5165\u91cd\u8981\u6027\u5f52\u56e0\u7b97\u6cd5\u7684\u8ba1\u7b97\u672c\u8d28\u5728\u6570\u5b66\u4e0a\u90fd\u53ef\u4ee5\u7edf\u4e00\u5199\u6210\u6210\u5bf9\u4ea4\u4e92\u4f5c\u7528\u7684\u518d\u5206\u914d\u5f62\u5f0f\uff0c\u8fd8\u63d0\u51fa\u4e8612\u79cd\u63d0\u5347\u5bf9\u6297\u6027\u8fc1\u79fb\u7684\u7b97\u6cd5\uff0c\u8bc1\u660e\u4e86\u6240\u6709\u63d0\u5347\u5bf9\u6297\u6027\u8fc1\u79fb\u7b97\u6cd5\u7684\u4e00\u4e2a\u516c\u5171\u673a\u7406\u662f\u964d\u4f4e\u5bf9\u6297\u6270\u52a8\u4e4b\u95f4\u7684\u4ea4\u4e92\u6548\u7528\uff0c\u5b9e\u73b0\u4e86\u5bf9\u795e\u7ecf\u7f51\u7edc\u53ef\u89e3\u91ca\u6027\u65b9\u5411\u5927\u90e8\u5206\u5de5\u7a0b\u6027\u7b97\u6cd5\u7684\u7406\u8bba\u51dd\u7ec3\u3002<\/span><\/span><\/p>\n

        6.\u795e\u7ecf\u7f51\u7edc\u53ef\u89e3\u91ca\u6027\uff1a\u6b63\u672c\u6e05\u6e90\uff0c\u8bba\u7edf\u4e0014\u79cd\u8f93\u5165\u91cd\u8981\u6027\u5f52\u56e0\u7b97\u6cd5<\/span><\/span><\/p>\n

        Deng et al.<\/span><\/span><\/span> “Understanding and Unifying Fourteen Attribution Methods with <\/span><\/span>Taylor Interactions<\/span><\/span><\/span>” in arXiv:2303.01506<\/span><\/span><\/p>\n

        https:\/\/zhuanlan.zhihu.com\/p\/610774894<\/span><\/span><\/p>\n

        7.\u6562\u95ee\u6df1\u5ea6\u5b66\u4e60\u8def\u5728\u4f55\u65b9\uff0c\u4ece\u7edf\u4e0012\u79cd\u63d0\u5347\u5bf9\u6297\u8fc1\u79fb\u6027\u7684\u7b97\u6cd5\u8bf4\u8d77<\/span><\/span><\/p>\n

        Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferabilityhttps:\/\/arxiv.org\/abs\/2207.11694<\/span><\/span><\/p>\n

        https:\/\/zhuanlan.zhihu.com\/p\/546433296<\/span><\/span><\/p>\n

        \u6cbf\u7740\u4e0a\u8ff0\u7406\u8bba\u6846\u67b6\uff0c\u5f20\u62f3\u77f3\u8001\u5e08\u56e2\u961f\u5e0c\u671b\u7cbe\u786e\u89e3\u91ca\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6cdb\u5316\u6027\u7684\u53d8\u5316\u89c4\u5f8b\u3002<\/span><\/span><\/p>\n

        8.\u8bba\u6587\u53d1\u73b0\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff08DNN\uff09\u5b66\u4e60\u4ea4\u4e92\u7279\u5f81\u5b58\u5728\u4e24\u9636\u6bb5\u52a8\u6001\u8fc7\u7a0b\uff0c\u5373\u7b2c\u4e00\u9636\u6bb5\u6291\u5236\u4e2d\u9ad8\u9636\u4ea4\u4e92\u3001\u5b66\u4e60\u4f4e\u9636\u4ea4\u4e92\u4ee5\u63d0\u5347\u6cdb\u5316\u80fd\u529b\uff0c\u7b2c\u4e8c\u9636\u6bb5\u9010\u6b65\u5b66\u4e60\u9ad8\u9636\u4ea4\u4e92\u5bfc\u81f4\u8fc7\u62df\u5408\uff0c\u8be5\u73b0\u8c61\u4e0e\u8bad\u7ec3 – \u6d4b\u8bd5\u635f\u5931\u5dee\u8ddd\u7684\u53d8\u5316\u65f6\u5e8f\u4e00\u81f4\uff0c\u63ed\u793a\u4e86 DNN \u4ece\u6b20\u62df\u5408\u5230\u8fc7\u62df\u5408\u7684\u8d77\u59cb\u673a\u5236\u3002<\/span><\/span><\/p>\n

        Two-Phase Dynamics of Interactions Explains the Starting Point of a DNN Learning Over-Fitted Features https:\/\/arxiv.org\/html\/2405.10262v1<\/span><\/span><\/p>\n

        9.\u8bba\u6587\u901a\u8fc7\u6570\u5b66\u8bc1\u660e\u63ed\u793a\u4e86\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff08DNN\uff09\u5b66\u4e60\u4ea4\u4e92\u7279\u5f81\u7684\u4e24\u9636\u6bb5\u52a8\u6001\u673a\u5236\uff1a\u521d\u59cb\u9636\u6bb5\u6291\u5236\u4e2d\u9ad8\u9636\u4ea4\u4e92\u4ee5\u5b66\u4e60\u4f4e\u9636\u6cdb\u5316\u7279\u5f81\uff0c\u7b2c\u4e8c\u9636\u6bb5\u9010\u6b65\u5f15\u5165\u9ad8\u9636\u4ea4\u4e92\u5bfc\u81f4\u8fc7\u62df\u5408\uff0c\u8be5\u7406\u8bba\u4e0e\u8bad\u7ec3 – \u6d4b\u8bd5\u635f\u5931\u5dee\u8ddd\u7684\u53d8\u5316\u4e00\u81f4\uff0c\u5e76\u901a\u8fc7\u591a\u4efb\u52a1\u5b9e\u9a8c\u9a8c\u8bc1\u4e86\u5176\u666e\u9002\u6027\u3002<\/span><\/span><\/p>\n

        Ren, Qihan, et al. “Towards the dynamics of a DNN learning symbolic interactions.” <\/span><\/span>Advances in Neural Information Processing Systems<\/span><\/span><\/em> 37 (2024): 50653-50688.<\/span><\/span><\/p>\n

        https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/5aa96d1caa0d0b99d534b67df06be2ff-Paper-Conference.pdf<\/span><\/span><\/p>\n


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

        \u590d\u6742\u79d1\u5b66\u89c6\u89d2<\/span><\/span><\/h3>\n
        <\/section>\n

        \u56fe\u7247\u6765\u6e90\uff1ahttps:\/\/www.santafe.edu\/research\/projects\/artificial-intelligence-foundations-frontiers\uff08Cyborg (Illustration: Runran\/flickr)\uff09<\/span><\/span><\/p>\n


        <\/span><\/h4>\n

        \u76f8\u53d8\u73b0\u8c61\u4e0e\u76f8\u5173\u7406\u8bba<\/span><\/span><\/h4>\n

        1.\u7528\u6e17\u6d41\u6a21\u578b\u89e3\u91ca\u5927\u8bed\u8a00\u6a21\u578b\u7684\u6d8c\u73b0\u73b0\u8c61\uff0c\u628a\u5927\u8bed\u8a00\u6a21\u578b\u7684\u6d8c\u73b0\u548c\u4e8c\u5206\u56fe\u4e0a\u7684\u6e17\u6d41\u76f8\u53d8\u8054\u7cfb\u5728\u4e86\u4e00\u8d77\u3002\uff08\u590d\u6742\u7f51\u7edc\u7684\u6e17\u6d41\u76f8\u53d8\uff09<\/span><\/span><\/p>\n

        Lubana, E. S., Kawaguchi, K., Dick, R. P., & Tanaka, H. (2024). A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language (arXiv:2408.12578). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2408.12578<\/span><\/span><\/p>\n

        2.\u7528\u4e00\u79cd\u7edf\u4e00\u7684\u89c6\u89d2\u6765\u7406\u89e3\u5927\u8bed\u8a00\u6a21\u578b\u7684\u987f\u609f\u3001\u53cc\u964d\u548c\u6d8c\u73b0\u73b0\u8c61\u3002\u5b83\u63d0\u51fa\u8fd9\u4e09\u79cd\u73b0\u8c61\u6765\u81ea\u4e8e\uff0c\u8bb0\u5fc6\u548c\u6cdb\u5316\u4e24\u79cd\u8def\u5f84\u5728\u5927\u8bed\u8a00\u6a21\u578b\u5185\u90e8\u7684\u7ade\u4e89\uff0c\u4f7f\u5f97\u6a21\u578b\u5904\u5728\u4e0d\u540c\u7684\u76f8\uff0c\u4ee5\u53ca\u76f8\u4e0e\u76f8\u4e4b\u95f4\u7684\u5207\u6362\u3002\uff08\u4e00\u5957\u673a\u5236\u89e3\u91cagrok\u53cc\u4e0b\u964d \u6d8c\u73b0\uff09<\/span><\/span><\/p>\n

        Huang, Y., Hu, S., Han, X., Liu, Z., & Sun, M. (2024). Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition (arXiv:2402.15175). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2402.15175<\/span><\/span><\/p>\n


        <\/span><\/h4>\n

        \u81ea\u7ec4\u7ec7\u7406\u8bba<\/span><\/span><\/h4>\n

        1.\u8fd9\u7bc7\u6587\u7ae0\u901a\u8fc7\u63d0\u51fa\u201c\u795e\u7ecf\u5143\u591a\u91cd\u5206\u5f62\u5206\u6790\uff08NeuroMFA\uff09\u201d\uff0c\u7528\u795e\u7ecf\u5143\u4e4b\u95f4\u52a8\u6001\u4e92\u4f5c\u7684\u591a\u5c3a\u5ea6\u590d\u6742\u6027\u6765\u89e3\u91ca\u548c\u5ea6\u91cf\u5927\u8bed\u8a00\u6a21\u578b\u4e2d\u7684\u6d8c\u73b0\u73b0\u8c61\uff0c\u4ece\u795e\u7ecf\u5143\u81ea\u7ec4\u7ec7\u7684\u89d2\u5ea6\u63ed\u793a\u4e86\u6a21\u578b\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7531\u7b80\u5355\u5c40\u90e8\u4e92\u4f5c\u9010\u6b65\u4ea7\u751f\u590d\u6742\u667a\u80fd\u884c\u4e3a\u7684\u5185\u5728\u673a\u5236\u3002\uff08\u81ea\u7ec4\u7ec7\uff09<\/span><\/span><\/p>\n

        Xiao, X., Ping, H., Zhou, C., Cao, D., Li, Y., Zhou, Y.-Z., Li, S., Kanakaris, N., & Bogdan, P. (2025). Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models (arXiv:2402.09099). arXiv.https:\/\/doi.org\/10.48550\/arXiv.2402.09099<\/span><\/span><\/p>\n

        2.\u5f3a\u8c03\u7ed3\u6784\u548c\u529f\u80fd\u7684\u5173\u7cfb\uff0c\u4ece\u590d\u6742\u7cfb\u7edf\u81ea\u7ec4\u7ec7\u7684\u89d2\u5ea6\u7406\u89e3\u5927\u6a21\u578b\u7684\u5c0f\u578b\u7efc\u8ff0\u6587\u7ae0<\/span><\/span><\/p>\n

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        <\/span><\/h4>\n

        \u81ea\u65cb\u73bb\u7483\u7406\u8bba<\/span><\/span><\/h4>\n

        1.\u901a\u8fc7\u81ea\u65cb\u73bb\u7483\u7406\u8bba\u6846\u67b6\uff0c\u5bf9\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLMs\uff09\u4e2d\u7684\u4e0a\u4e0b\u6587\u5b66\u4e60\uff08ICL\uff09\u673a\u5236\u63d0\u51fa\u4e86\u65b0\u7684\u7406\u8bba\u89e3\u91ca\u3002\u5c06ICL\u4e2d\u8bed\u8a00\u6a21\u578b\u7684\u52a8\u6001\u884c\u4e3a\u6620\u5c04\u5230\u81ea\u65cb\u73bb\u7483\u7cfb\u7edf\u7684\u80fd\u91cf\u666f\u89c2\uff0c\u5176\u4e2d\u8f93\u5165\u63d0\u793a\uff08prompt\uff09\u76f8\u5f53\u4e8e\u5916\u90e8\u78c1\u573a\uff0c\u6a21\u578b\u53c2\u6570\u5bf9\u5e94\u81ea\u65cb\u72b6\u6001\uff0c\u800c\u4e0a\u4e0b\u6587\u4fe1\u606f\u7684\u4f5c\u7528\u7c7b\u4f3c\u4e8e\u78c1\u7574\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u3002<\/span><\/span><\/p>\n

        Li, Y., Bai, R., & Huang, H. (2025). <\/span><\/span>Spin glass model of in-context learning<\/span><\/span><\/em> (arXiv:2408.02288). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2408.02288<\/span><\/span><\/p>\n


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

        \u7cfb\u7edf\u5de5\u7a0b\u89c6\u89d2<\/span><\/span><\/h3>\n
        <\/section>\n

        \u56fe\u7247\u6765\u6e90\uff1ahttps:\/\/www.anthropic.com\/news\/core-views-on-ai-safety<\/span><\/span><\/p>\n


        <\/span><\/h4>\n

        \u6a21\u5757\u91cd\u8981\u6027\u5212\u5206<\/span><\/span><\/h4>\n

        1.\u901a\u8fc7\u56e0\u679c\u5e72\u9884\u548c\u8def\u5f84\u4fee\u8865\u6280\u672f\uff0c\u5728GPT-2 small\u4e2d\u8bc6\u522b\u5e76\u9a8c\u8bc1\u4e8626\u4e2a\u6ce8\u610f\u529b\u5934\u7ec4\u6210\u7684\u7a00\u758f\u7535\u8def\uff0c\u7528\u4e8e\u95f4\u63a5\u5bbe\u8bed\u8bc6\u522b\u4efb\u52a1\u3002\uff08\u56de\u8def\u53d1\u73b0\u4e2d\u4e5f\u6709\u8fd9\u7bc7\u6587\u7ae0\uff09<\/span><\/span><\/p>\n

        Wang, Kevin, et al. “Interpretability in the wild: a circuit for indirect object identification in gpt-2 small.” <\/span><\/span>arXiv preprint arXiv:2211.00593<\/span><\/span><\/em> (2022).<\/span><\/span><\/p>\n

        2.\u63d0\u51fa\u57fa\u4e8e\u56e0\u679c\u4e2d\u4ecb\u5206\u6790\u7684\u65b9\u6cd5\uff0c\u63ed\u793a\u6027\u522b\u504f\u89c1\u5728Transformer\u6a21\u578b\u4e2d\u7684\u7a00\u758f\u6027\u548c\u534f\u540c\u6027\uff0c\u5b9a\u4f4d\u5173\u952e\u795e\u7ecf\u5143\u548c\u6ce8\u610f\u529b\u5934\u3002<\/span><\/span><\/p>\n

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        3.\u63d0\u51fa\u57fa\u4e8e\u68af\u5ea6\u7684\u201c\u5f52\u56e0\u4fee\u8865\u201d\u6280\u672f\uff0c\u9ad8\u6548\u8fd1\u4f3c\u6fc0\u6d3b\u4fee\u8865\uff0c\u7528\u4e8e\u5927\u89c4\u6a21\u6a21\u578b\u4e2d\u5b9a\u4f4d\u5173\u952e\u6a21\u5757\uff08\u5982\u6ce8\u610f\u529b\u5934\uff09\u3002<\/span><\/span><\/p>\n

        Nanda, Neel. “Attribution patching: Activation patching at industrial scale.” <\/span><\/span>URL: <\/span><\/span><\/em>https:\/\/www<\/span><\/span><\/em>. neelnanda. io\/mechanistic-interpretability\/attribution-patching<\/span><\/span><\/em> (2023).<\/span><\/span><\/p>\n

        4.\u53d1\u73b0ICL\u4e2d\u6807\u7b7e\u8bcd\u4f5c\u4e3a\u4fe1\u606f\u951a\u70b9\uff0c\u6d45\u5c42\u8bed\u4e49\u4fe1\u606f\u805a\u5408\u5230\u6807\u7b7e\u8bcd\u8868\u5f81\uff0c\u6700\u7ec8\u5f71\u54cd\u9884\u6d4b\u3002<\/span><\/span><\/p>\n

        Wang, Lean, et al. “Label words are anchors: An information flow perspective for understanding in-context learning.” <\/span><\/span>arXiv preprint arXiv:2305.14160<\/span><\/span><\/em> (2023).<\/span><\/span><\/p>\n

        5.\u53d1\u73b0LLMs\u4e2d\u5c11\u91cf\u5173\u952e\u6ce8\u610f\u529b\u5934\uff08<5%\uff09\u548cMLP\u5c42\u5728\u7b97\u672f\u8ba1\u7b97\u4e2d\u8d77\u6838\u5fc3\u4f5c\u7528\uff0c\u9009\u62e9\u6027\u5fae\u8c03\u53ef\u63d0\u5347\u6570\u5b66\u80fd\u529b\u3002<\/span><\/span><\/p>\n

        Zhang, Wei, et al. “Interpreting and improving large language models in arithmetic calculation.” <\/span><\/span>arXiv preprint arXiv:2409.01659<\/span><\/span><\/em> (2024).<\/span><\/span><\/p>\n

        6.\u901a\u8fc7\u89c4\u6a21\u5316\u65b9\u6cd5\u4eceClaude 3\u4e2d\u63d0\u53d6\u53ef\u89e3\u91ca\u7684\u7a00\u758f\u7279\u5f81\uff0c\u63ed\u793a\u6a21\u578b\u5185\u90e8\u6a21\u5757\u7684\u8bed\u4e49\u5206\u5de5\u3002<\/span><\/span><\/p>\n

        Templeton, Adly. <\/span><\/span>Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet<\/span><\/span><\/em>. Anthropic, 2024.<\/span><\/span><\/p>\n

        7.\u63d0\u51faSAEBench\uff0c\u4e00\u4e2a\u8bc4\u4f30\u7a00\u758f\u81ea\u7f16\u7801\u5668\uff08SAEs\uff09\u5728\u8bed\u8a00\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u4e2d\u6027\u80fd\u7684\u7efc\u5408\u57fa\u51c6\uff0c\u6db5\u76d6\u65e0\u76d1\u7763\u6307\u6807\u548c\u4e0b\u6e38\u4efb\u52a1\u3002<\/span><\/span><\/p>\n

        Karvonen, Adam, et al. “SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability.” <\/span><\/span>arXiv preprint arXiv:2503.09532<\/span><\/span><\/em> (2025).<\/span><\/span><\/p>\n

        8.\u901a\u8fc7\u5b9a\u4f4d\u548c\u7f16\u8f91GPT\u4e2d\u4e0e\u4e8b\u5b9e\u5173\u8054\u7684\u4e2d\u95f4\u5c42MLP\u6a21\u5757\uff0c\u5b9e\u73b0\u77e5\u8bc6\u7684\u9ad8\u6548\u66f4\u65b0\u3002<\/span><\/span><\/p>\n

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        9.\u63d0\u51fa\u7a00\u758f\u7279\u5f81\u7535\u8def\u65b9\u6cd5\uff0c\u901a\u8fc7\u53ef\u89e3\u91ca\u7684\u56e0\u679c\u5b50\u7f51\u7edc\u5b9a\u4f4d\u548c\u7f16\u8f91\u8bed\u8a00\u6a21\u578b\u884c\u4e3a\u3002<\/span><\/span><\/p>\n

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        1.\u901a\u8fc7\u7a00\u758f\u6fc0\u6d3b\u63a7\u5236\uff08SAC\uff09\u6280\u672f\uff0c\u72ec\u7acb\u8c03\u63a7\u6ce8\u610f\u529b\u5934\u4ee5\u540c\u65f6\u63d0\u5347\u6a21\u578b\u7684\u5b89\u5168\u6027\u3001\u4e8b\u5b9e\u6027\u548c\u65e0\u504f\u89c1\u6027\u3002<\/span><\/span><\/p>\n

        Xiao, Yuxin, et al. “Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control.” <\/span><\/span>Advances in Neural Information Processing Systems<\/span><\/span><\/em> 37 (2024): 15730-15764.<\/span><\/span><\/p>\n

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        2.\u63ed\u793aDPO\u5bf9\u9f50\u7b97\u6cd5\u901a\u8fc7\u5206\u5e03\u5f0f\u5fae\u8c03\u7ed5\u8fc7\uff08\u800c\u975e\u6d88\u9664\uff09\u6bd2\u6027\u751f\u6210\u533a\u57df\uff0c\u89e3\u91ca\u6a21\u578b\u6613\u88ab\u9006\u5411\u7834\u89e3\u7684\u673a\u5236\u3002<\/span><\/span><\/p>\n

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        3.\u7814\u7a76\u53d1\u73b0\u94fe\u5f0f\u601d\u7ef4\uff08CoT\uff09\u63d0\u793a\u751f\u6210\u7684\u63a8\u7406\u8fc7\u7a0b\u5e76\u4e0d\u603b\u662f\u5fe0\u5b9e\u53cd\u6620\u6a21\u578b\u7684\u771f\u5b9e\u63a8\u7406\u610f\u56fe\uff0c\u63d0\u793a\u76d1\u63a7CoT\u53ef\u80fd\u4e0d\u8db3\u4ee5\u68c0\u6d4b\u7f55\u89c1\u6216\u610f\u5916\u7684\u6a21\u578b\u884c\u4e3a\u3002<\/span><\/span><\/p>\n

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        4.\u901a\u8fc7\u8868\u5f81\u5de5\u7a0b\uff08\u5982LAT\u65b9\u6cd5\uff09\u76d1\u63a7\u548c\u63a7\u5236\u6a21\u578b\u5185\u90e8\u7684\u9ad8\u5c42\u8ba4\u77e5\u6982\u5ff5\uff08\u5982\u8bda\u5b9e\u6027\uff09\u3002<\/span><\/span><\/p>\n

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        5.\u901a\u8fc7\u5fae\u8c03\u5173\u952e\u5b50\u6a21\u5757\uff08<5%\uff09\u89e3\u51b3\u5927\u6a21\u578b\u4e2d\u7684\u8c04\u5a9a\u884c\u4e3a\uff0c\u4fdd\u6301\u901a\u7528\u80fd\u529b\u3002<\/span><\/span><\/p>\n

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        <\/span><\/h4>\n

        \u6a21\u578b\u878d\u5408<\/span><\/span><\/h4>\n

        1.\u7814\u7a76\u591a\u8bed\u8a00\u6a21\u578b\u4e2d\u8bed\u8a00\u7279\u5b9a\u548c\u8bed\u8a00\u65e0\u5173\u7684\u6a21\u5757\u5212\u5206\uff0c\u5206\u6790\u8de8\u8bed\u8a00\u77e5\u8bc6\u8fc1\u79fb\u7684\u673a\u5236\u3002<\/span><\/span><\/p>\n

        Zhao, Yiran, et al. “How do large language models handle multilingualism?.” <\/span><\/span>arXiv preprint arXiv:2402.18815<\/span><\/span><\/em> (2024).<\/span><\/span><\/span>https:\/\/arxiv.org\/pdf\/2402.18815<\/span><\/span><\/p>\n

        2.\u7c7b\u6bd4\u751f\u7269\u7cfb\u7edf\u7814\u7a76LLM\u5185\u90e8\u7ec4\u4ef6\u7684\u534f\u540c\u673a\u5236\uff0c\u63d0\u51fa\u6a21\u5757\u95f4\u52a8\u6001\u4ea4\u4e92\u5f62\u6210\u590d\u6742\u884c\u4e3a\u7684\u7406\u8bba\u6846\u67b6\u3002\uff08\u56de\u8def\u53d1\u73b0\u4e2d\u4e5f\u6709\u8fd9\u7bc7\u6587\u7ae0\uff09<\/span><\/span><\/p>\n

        Lindsey, Jack, et al. “On the biology of a large language model.” <\/span><\/span>Transformer Circuits Thread <\/span><\/span><\/em>(2025).<\/span><\/span><\/p>\n

        https:\/\/transformercircuits.pub\/2025\/attribution-graphs\/biology.htm<\/span><\/span><\/p>\n

        3.\u63d0\u51fa\u4e00\u79cd\u81ea\u52a8\u5316\u751f\u6210\u9ad8\u8d28\u91cf\u795e\u7ecf\u5143\u63cf\u8ff0\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u5fae\u8c03\u6a21\u62df\u5668\u548c\u89e3\u91ca\u5668\u6a21\u578b\uff0c\u5b9e\u73b0\u5bf9\u5927\u89c4\u6a21\u8bed\u8a00\u6a21\u578b\u5185\u90e8\u795e\u7ecf\u5143\u7684\u4f4e\u6210\u672c\u3001\u9ad8\u6548\u63cf\u8ff0\u3002<\/span><\/span><\/p>\n

        Scaling Automatic Neuron Description. Transluce AI. https:\/\/transluce.org\/neuron-descriptions<\/span><\/span><\/p>\n

        4.\u5229\u7528\u5b50\u6a21\u5757\u7684\u7ebf\u6027\u7279\u6027\u72ec\u7acb\u5408\u5e76\u6a21\u578b\uff0c\u63d0\u5347\u591a\u4efb\u52a1\u7b97\u672f\u6027\u80fd\u3002<\/span><\/span><\/p>\n

        Dai, Rui, et al. “Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMs.” <\/span><\/span>arXiv preprint arXiv:2504.10902<\/span><\/span><\/em> (2025).<\/span><\/span><\/p>\n

        https:\/\/arxiv.org\/abs\/2504.109025.<\/span><\/span><\/p>\n

        5.\u63d0\u51fa\u201c\u4f2a\u9057\u5fd8\u201d\u6982\u5ff5\uff0c\u901a\u8fc7\u51bb\u7ed3\u5e95\u5c42\u53c2\u6570\u7f13\u89e3\u6301\u7eed\u5b66\u4e60\u4e2d\u7684\u4efb\u52a1\u5bf9\u9f50\u9000\u5316\u95ee\u9898\u3002<\/span><\/span><\/p>\n

        Zheng, Junhao, et al. “Spurious Forgetting in Continual Learning of Language Models.” <\/span><\/span>arXiv preprint arXiv:2501.13453<\/span><\/span><\/em> (2025).<\/span><\/span><\/span>https:\/\/openreview.net\/forum?id=ScI7IlKGdI<\/span><\/span><\/p>\n


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

        \u4e66\u7c4d\u63a8\u8350<\/span><\/span><\/p>\n

        \u6768\u5f3a\uff0c\u8303\u529b\u6b23\uff0c\u6731\u519b\uff0c\u9648\u4e00\u6615\uff0c\u5f20\u62f3\u77f3\uff0c\u6731\u677e\u7eaf\uff0c\u9676\u5927\u7a0b\uff0c\u5d14\u9e4f\uff0c\u5468\u5c11\u534e\uff0c\u5218\u7426\u9ec4\u8431\u83c1\uff0c\u5f20\u6c38\u950b\uff0c\u53ef\u89e3\u91ca\u4eba\u5de5\u667a\u80fd\u5bfc\u8bba\uff0c2022\uff0c\u7535\u5b50\u5de5\u4e1a\u51fa\u7248\u793e<\/span><\/span><\/p>\n

        <\/section>\n

        Christoph Molnar,\u90ed\u6d9b(\u8bd1)\uff0c\u53ef\u89e3\u91ca\u673a\u5668\u5b66\u4e60:\u9ed1\u76d2\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u7406\u89e3\u6307\u5357(\u7b2c2\u7248)\uff0c2024\uff0c\u7535\u5b50\u5de5\u4e1a\u51fa\u7248\u793e<\/span><\/span><\/p>\n

        <\/section>\n

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

        \n
        \n
        \n
        \n
        \n

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

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

        \u5173\u4e8e\u96c6\u667a\u4ff1\u4e50\u90e8\u8bfb\u4e66\u4f1a\u548c\u4e3e\u529e\u65b9<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

        \n

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

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


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

        \u96c6\u667a\u4ff1\u4e50\u90e8\u8bfb\u4e66\u4f1a<\/span><\/span><\/strong>\u662f\u9762\u5411\u5e7f\u5927\u79d1\u7814\u5de5\u4f5c\u8005\u7684\u7cfb\u5217\u8bba\u6587\u7814\u8bfb\u6d3b\u52a8\uff0c\u5176\u76ee\u7684\u662f\u5171\u540c\u6df1\u5165\u5b66\u4e60\u63a2\u8ba8\u67d0\u4e2a\u79d1\u5b66\u8bae\u9898\uff0c\u4e86\u89e3\u524d\u6cbf\u8fdb\u5c55\uff0c\u6fc0\u53d1\u79d1\u7814\u7075\u611f\uff0c\u4fc3\u8fdb\u79d1\u7814\u5408\u4f5c\uff0c\u964d\u4f4e\u79d1\u7814\u95e8\u69db\u3002<\/span><\/span><\/p>\n

        \u8bfb\u4e66\u4f1a\u6d3b\u52a8\u59cb\u4e8e 2008 \u5e74\uff0c\u81f3\u4eca\u5df2\u7ecf\u6709 50 \u4f59\u4e2a\u4e3b\u9898\uff0c\u5185\u5bb9\u6db5\u76d6\u590d\u6742\u7cfb\u7edf\u3001\u4eba\u5de5\u667a\u80fd\u3001\u8111\u4e0e\u610f\u8bc6\u3001\u751f\u547d\u79d1\u5b66\u3001\u56e0\u679c\u79d1\u5b66\u3001\u9ad8\u9636\u7f51\u7edc\u7b49\u3002\u51dd\u805a\u4e86\u4f17\u591a\u4f18\u79c0\u79d1\u7814\u5de5\u4f5c\u8005\uff0c\u4fc3\u8fdb\u4e86\u79d1\u7814\u5408\u4f5c\u53d1\u8868\u8bba\u6587\uff0c\u5b75\u5316\u4e86\u8bb8\u591a\u79d1\u7814\u4ea7\u54c1\u3002\u5982\uff1a2013 \u5e74\u7684\u201c\u6df1\u5ea6\u5b66\u4e60\u201d\u8bfb\u4e66\u4f1a\u5b55\u80b2\u4e86\u5f69\u4e91\u5929\u6c14 APP\uff0c2015 \u5e74\u7684\u201c\u96c6\u4f53\u6ce8\u610f\u529b\u6d41\u201d\u8bfb\u4e66\u4f1a\u4ea7\u751f\u4e86\u4f17\u5305\u4e66\u7c4d\u300a\u8d70\u8fd12050\u300b\uff0c2020\u5e74\u7684\u5f00\u59cb\u56e0\u679c\u79d1\u5b66\u8bfb\u4e66\u4f1a\u5b55\u80b2\u4e86\u5168\u56fd\u6700\u5927\u7684\u56e0\u679c\u79d1\u5b66\u793e\u533a\u7b49\u3002<\/span><\/span><\/p>\n

        \u4e3b\u529e\u65b9\uff1a\u96c6\u667a\u4ff1\u4e50\u90e8<\/span><\/span><\/strong><\/p>\n

        \u534f\u529e\u65b9\uff1a\u96c6\u667a\u5b66\u56ed<\/span><\/span><\/strong><\/p>\n

        \u96c6\u667a\u4ff1\u4e50\u90e8<\/span><\/span><\/strong>\u6210\u7acb\u4e8e 2003 \u5e74\uff0c\u662f\u4e00\u4e2a\u4ece\u4e8b\u5b66\u672f\u7814\u7a76\u3001\u4eab\u53d7\u79d1\u5b66\u4e50\u8da3\u7684\u63a2\u7d22\u8005\u7684\u56e2\u4f53\uff0c\u4e5f\u662f\u56fd\u5185\u6700\u65e9\u7684\u7814\u7a76\u4eba\u5de5\u667a\u80fd\u3001\u590d\u6742\u7cfb\u7edf\u7684\u79d1\u5b66\u793e\u533a\u3002\u5b83\u5021\u5bfc\u4ee5\u5e73\u7b49\u5f00\u653e\u7684\u6001\u5ea6\u3001\u79d1\u5b66\u5b9e\u8bc1\u7684\u7cbe\u795e\uff0c\u8fdb\u884c\u8de8\u5b66\u79d1\u7684\u7814\u7a76\u4e0e\u4ea4\u6d41\uff0c\u529b\u56fe\u642d\u5efa\u4e00\u4e2a\u4e2d\u56fd\u7684 \u201c \u6ca1\u6709\u56f4\u5899\u7684\u7814\u7a76\u6240 \u201d\u3002\u96c6\u667a\u79d1\u5b66\u7814\u7a76\u4e2d\u5fc3\uff08\u6c11\u95f4\u975e\u8425\u5229\u4f01\u4e1a\uff09\u662f\u96c6\u667a\u4ff1\u4e50\u90e8\u7684\u8fd0\u8425\u4e3b\u4f53\uff0c\u5176\u4f7f\u547d\u4e3a\uff1a\u8425\u9020\u8de8\u5b66\u79d1\u63a2\u7d22\u5c0f\u751f\u5883\uff0c\u50ac\u5316\u590d\u6742\u6027\u79d1\u5b66\u65b0\u7406\u8bba\u3002<\/span><\/span><\/p>\n

        \u96c6\u667a\u5b66\u56ed<\/span><\/span><\/strong>\u6210\u7acb\u4e8e2016\u5e74\uff0c\u662f\u96c6\u667a\u4ff1\u4e50\u90e8\u5b55\u80b2\u7684\u521b\u4e1a\u56e2\u961f\u3002\u96c6\u667a\u5b66\u56ed\u81f4\u529b\u4e8e\u4f20\u64ad\u590d\u6742\u6027\u79d1\u5b66\u3001\u4eba\u5de5\u667a\u80fd\u7b49\u524d\u6cbf\u77e5\u8bc6\u548c\u65b0\u5174\u6280\u672f\uff0c\u4fc3\u8fdb\u3001\u63a8\u52a8\u590d\u6742\u79d1\u5b66\u9886\u57df\u7684\u77e5\u8bc6\u63a2\u7d22\u4e0e\u751f\u6001\u6784\u5efa\u3002<\/span><\/span><\/p>\n

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

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

        <\/span><\/section>\n
        \u63a8\u8350\u9605\u8bfb<\/span><\/span><\/strong><\/strong><\/strong><\/section>\n
        <\/strong><\/section>\n
        1.AI\u4f55\u4ee5\u6d8c\u73b0\uff1a\u590d\u6742\u9002\u5e94\u7cfb\u7edf\u89c6\u89d2\u7684ChatGPT\u548c\u5927\u8bed\u8a00\u6a21\u578b<\/span><\/a><\/span><\/strong>
        <\/span><\/strong><\/span><\/section>\n
        2.\u4e07\u5b57\u957f\u6587\u8be6\u89e3\uff1a\u5927\u6a21\u578b\u65f6\u4ee3AI\u4ef7\u503c\u5bf9\u9f50\u7684\u95ee\u9898\u3001\u5bf9\u7b56\u548c\u5c55\u671b<\/span><\/a><\/span><\/strong>
        <\/span><\/strong><\/span><\/section>\n
        3.AI \u4e3a\u4ec0\u4e48\u4f1a\u6709\u521b\u9020\u529b\uff1f\u8303\u7574\u8bba\u523b\u753b\u5927\u6a21\u578b\u521b\u9020\u529b\u7684\u6765\u6e90<\/span><\/a><\/span>
        <\/span><\/strong><\/strong><\/section>\n
        <\/strong><\/section>\n
        4. <\/span><\/strong><\/span>\u6e38\u620f\u5316\u79d1\u7814\u2014\u2014\u8ba9\u6211\u4eec\u7a81\u7834\u5185\u5377\u3001\u5171\u7814\u6d8c\u73b0<\/a><\/span><\/strong><\/strong><\/span><\/section>\n
        <\/strong>5. <\/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><\/section>\n

        6. \u52a0\u5165\u96c6\u667a\uff0c\u73a9\u8f6c\u590d\u6742\uff0c\u5171\u521b\u6591\u56fe\uff01\u96c6\u667a\u4ff1\u4e50\u90e8\u7ebf\u4e0b\u5fd7\u613f\u8005\u62db\u52df<\/span><\/a><\/span><\/strong><\/p>\n


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

        <\/span><\/section>\n
        \u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u62a5\u540d\u8bfb\u4e66\u4f1a<\/span><\/span><\/section>\n<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

        \u5bfc\u8bed \u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u526f\u6559\u6388\u5f20\u62f3\u77f3\u3001\u963f\u91cc\u4e91\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u56e2\u961f\u8d1f\u8d23\u4eba\u6c88\u65ed\u3001\u5f69\u4e91\u79d1\u6280\u9996\u5e2d\u79d1\u5b66\u5bb6\u8096\u8fbe\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7855\u58eb\u751f\u6768\u660e\u54f2\u548c\u6d59\u6c5f\u5927\u5b66\u535a\u58eb\u751f\u59da\u4e91\u5fd7\u5171\u540c\u53d1\u8d77\u300c\u5927\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u300d\u8bfb\u4e66\u4f1a\u3002\u672c\u8bfb\u4e66\u4f1a\u65e8\u5728\u7a81\u7834\u5927\u6a21\u578b\u201c\u9ed1\u7bb1\u201d\u56f0\u5883\uff0c\u5c1d\u8bd5\u4ece\u4ee5\u4e0b\u56db\u4e2a\u89c6\u89d2\u68b3\u7406\u5927\u8bed\u8a00\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u7684\u79d1\u5b66\u65b9\u6cd5\u8bba\uff1a \u81ea\u4e0b\u800c\u4e0a\uff1aTransf…<\/p>\n","protected":false},"author":0,"featured_media":60191,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/60214"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=60214"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/60214\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/60191"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=60214"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=60214"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=60214"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=60214"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}