{"id":34186,"date":"2024-12-01T18:58:30","date_gmt":"2024-12-01T10:58:30","guid":{"rendered":"https:\/\/17aitech.com\/?p=34186"},"modified":"2024-12-01T18:58:30","modified_gmt":"2024-12-01T10:58:30","slug":"%e4%b8%8d%e5%81%9a%e6%95%b0%e5%80%bc%e8%bf%90%e7%ae%97%e3%80%81%e7%ba%af%e9%9d%a0%e5%98%b4%e7%82%ae%e4%b9%9f%e8%83%bd%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%ef%bc%9f%e5%9f%ba%e4%ba%8e%e8%87%aa%e7%84%b6","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=34186","title":{"rendered":"\u4e0d\u505a\u6570\u503c\u8fd0\u7b97\u3001\u7eaf\u9760\u5634\u70ae\u4e5f\u80fd\u673a\u5668\u5b66\u4e60\uff1f\u57fa\u4e8e\u81ea\u7136\u8bed\u8a00\u7684\u5168\u65b0ML\u8303\u5f0f\u6765\u4e86"},"content":{"rendered":"

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:\u4e0d\u505a\u6570\u503c\u8fd0\u7b97\u3001\u7eaf\u9760\u5634\u70ae\u4e5f\u80fd\u673a\u5668\u5b66\u4e60\uff1f\u57fa\u4e8e\u81ea\u7136\u8bed\u8a00\u7684\u5168\u65b0ML\u8303\u5f0f\u6765\u4e86<\/a><\/p>\n

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AIxiv\u4e13\u680f\u662f\u673a\u5668\u4e4b\u5fc3<\/mark>\u53d1\u5e03\u5b66\u672f\u3001\u6280\u672f\u5185\u5bb9\u7684\u680f\u76ee\u3002\u8fc7\u53bb\u6570\u5e74\uff0c\u673a\u5668\u4e4b\u5fc3<\/mark>AIxiv\u4e13\u680f\u63a5\u6536\u62a5\u9053\u4e862000\u591a\u7bc7\u5185\u5bb9\uff0c\u8986\u76d6\u5168\u7403\u5404\u5927\u9ad8\u6821\u4e0e\u4f01\u4e1a\u7684\u9876\u7ea7\u5b9e\u9a8c\u5ba4\uff0c\u6709\u6548\u4fc3\u8fdb\u4e86\u5b66\u672f\u4ea4\u6d41\u4e0e\u4f20\u64ad\u3002\u5982\u679c\u60a8\u6709\u4f18\u79c0\u7684\u5de5\u4f5c\u60f3\u8981\u5206\u4eab\uff0c\u6b22\u8fce\u6295\u7a3f\u6216\u8005\u8054\u7cfb\u62a5\u9053\u3002\u6295\u7a3f\u90ae\u7bb1\uff1aliyazhou@jiqizhixin.com\uff1bzhaoyunfeng@jiqizhixin.com<\/section>\n<\/section>\n<\/blockquote>\n

\u672c\u6587\u4f5c\u8005\u8096\u9547\u4e2d\u662f\u5fb7\u56fd\u9a6c\u514b\u601d\u666e\u6717\u514b-\u667a\u80fd\u7cfb\u7edf\u7814\u7a76\u6240\u548c\u56fe\u5bbe\u6839\u5927\u5b66\u7684\u535a\u58eb\u751f\uff0cRobert Bamler \u662f\u56fe\u5bbe\u6839\u5927\u5b66\u673a\u5668\u5b66\u4e60<\/mark>\u65b9\u5411\u7684\u6559\u6388\uff0cBernhard Sch\u00f6lkopf \u662f\u9a6c\u514b\u601d\u666e\u6717\u514b-\u667a\u80fd\u7cfb\u7edf\u7814\u7a76\u6240\u7684\u6240\u957f\uff0c\u5218\u5a01\u6768\u662f\u9a6c\u666e\u6240\u5251\u6865\u5927\u5b66\u8054\u5408\u9879\u76ee\u7684\u7814\u7a76\u5458\u3002<\/strong><\/p>\n

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\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/abs\/2406.04344<\/section>\n
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\u5728\u4f20\u7edf\u7684\u673a\u5668\u5b66\u4e60<\/mark>\u573a\u666f\u5982\u5206\u7c7b\u548c\u56de\u5f52\u95ee\u9898\u4e2d\uff0c\u7ed9\u5b9a\u8bad\u7ec3\u6570\u636e<\/a>\uff0c\u6211\u4eec\u901a\u8fc7\u4f18\u5316\u53c2\u6570<\/mark><\/a>\u5b66\u5230\u4e00\u4e2a\u51fd\u6570\u6a21\u578b<\/a>\u6765\u51c6\u786e\u63cf\u8ff0\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u4e2d<\/a>\u548c<\/a>\u7684\u5173\u7cfb\u3002\u5176\u4e2d<\/a>\u662f\u57fa\u4e8e\u6570\u503c\u7684\u51fd\u6570\uff0c\u5b83\u7684\u53c2\u6570<\/mark><\/a>\u901a\u5e38\u662f\u8fde\u7eed\u7a7a\u95f4\u4e2d\u7684\u6570\u503c\u5411\u91cf\u6216\u77e9\u9635\uff0c\u4f18\u5316\u7b97\u6cd5\u901a\u8fc7\u8ba1\u7b97\u6570\u503c\u68af\u5ea6\u8fed\u4ee3\u66f4\u65b0<\/a>\u4ece\u800c\u8fbe\u5230\u5b66\u4e60\u7684\u6548\u679c\u3002<\/section>\n
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\u4e0e\u5176\u7528\u6570\u503c\uff0c\u6211\u4eec\u662f\u5426\u80fd\u7528\u81ea\u7136\u8bed\u8a00\u6765\u8868\u793a\u4e00\u4e2a\u6a21\u578b\uff1f\u8fd9\u79cd\u57fa\u4e8e\u81ea\u7136\u8bed\u8a00\u7684\u975e\u6570\u503c\u6a21\u578b\u53c8\u8be5\u5982\u4f55\u505a\u63a8\u7406\u548c\u8bad\u7ec3<\/strong>\uff1f<\/section>\n
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Verbalized Machine Learning (VML\uff1b\u8a00\u8bed\u5316\u7684\u673a\u5668\u5b66\u4e60<\/mark>) \u56de\u7b54\u4e86\u8fd9\u4e9b\u95ee\u9898\uff0c\u5e76\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u81ea\u7136\u8bed\u8a00\u7684\u673a\u5668\u5b66\u4e60<\/mark>\u5168\u65b0\u8303\u5f0f\u3002VML \u628a\u5927\u8bed\u8a00\u6a21\u578b<\/mark> (LLM) \u5f53\u4f5c\u81ea\u7136\u8bed\u8a00\u7a7a\u95f4\u4e2d\u7684\u901a\u7528\u8fd1\u4f3c\u51fd\u6570 (universial function approximator)<\/a>\uff0c\u6570\u636e<\/a>\u548c\u53c2\u6570<\/mark><\/a>\u90fd\u662f\u81ea\u7136\u8bed\u8a00\u7a7a\u95f4\u4e2d\u7684\u5b57\u7b26\u4e32\u3002\u5728\u505a\u63a8\u7406\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u7ed9\u5b9a\u7684\u8f93\u5165\u6570\u636e<\/a>\u548c\u53c2\u6570<\/mark><\/a>\u63d0\u4ea4\u7ed9 LLM\uff0cLLM \u7684\u56de\u7b54\u5c31\u662f\u63a8\u7406\u7684\u7b54\u6848<\/a>\u3002<\/section>\n
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\u5bf9\u4e8e\u4efb\u610f\u4efb\u52a1\u548c\u6570\u636e<\/a>\uff0c\u6211\u4eec\u5982\u4f55\u5f97\u5230<\/a>\uff1f\u5728\u57fa\u4e8e\u6570\u503c\u7684\u4f20\u7edf\u673a\u5668\u5b66\u4e60<\/mark>\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7\u8ba1\u7b97\u635f\u5931\u51fd\u6570<\/mark>\u7684\u68af\u5ea6\uff0c\u5c06\u73b0\u6709\u7684\u6a21\u578b\u53c2\u6570<\/mark>\u5f80\u635f\u5931\u4e0b\u964d\u7684\u65b9\u5411\u66f4\u65b0\uff0c\u4ece\u800c\u5f97\u5230<\/a>\u7684\u4f18\u5316\u51fd\u6570:<\/section>\n

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\u5176\u4e2d<\/a>\u548c<\/a>\u5206\u522b\u4e3a\u5b66\u4e60\u7387<\/mark>\u548c\u635f\u5931\u51fd\u6570<\/mark>\u3002<\/section>\n
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\u5728 VML \u7684\u8bbe\u5b9a\u4e2d\uff0c\u7531\u4e8e\u6570\u636e<\/a>\u548c\u53c2\u6570<\/mark><\/a>\u90fd\u662f\u5b57\u7b26\u4e32\u4e14 LLM \u88ab\u5f53\u4f5c\u662f\u9ed1\u7bb1\u7684\u63a8\u7406\u5f15\u64ce<\/mark>\uff0c\u6240\u4ee5\u6211\u4eec\u65e0\u6cd5\u901a\u8fc7\u6570\u503c\u8ba1\u7b97\u6765\u4f18\u5316<\/a>\u3002\u4f46\u65e2\u7136\u6211\u4eec\u5df2\u7ecf\u5c06 LLM \u7528\u4f5c\u81ea\u7136\u8bed\u8a00\u7a7a\u95f4\u4e2d\u7684\u901a\u7528\u8fd1\u4f3c\u51fd\u6570\u53bb\u8fd1\u4f3c\u6a21\u578b\u51fd\u6570\uff0c\u800c<\/a>\u7684\u4f18\u5316\u5668<\/mark><\/a>\u4e5f\u662f\u4e00\u4e2a\u51fd\u6570\uff0c\u6211\u4eec\u4e3a\u4f55\u4e0d\u4e5f\u7528 LLM \u53bb\u8fd1\u4f3c\u5b83\uff1f\u56e0\u6b64\uff0c\u8a00\u8bed\u5316\u7684<\/a>\u4f18\u5316\u51fd\u6570\u53ef\u5199\u4f5c<\/section>\n

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\u5176\u4e2d<\/a>\u4e3a\u4e00\u4e2a\u6570\u91cf\u4e3a<\/a>\u7684\u6279\u6b21<\/mark>\u7684\u8bad\u7ec3\u6570\u636e\u548c\u6a21\u578b\u9884\u6d4b\u7ed3\u679c\uff0c<\/a>\u4e3a\u4f18\u5316\u51fd\u6570\u7684\u53c2\u6570<\/mark>\uff08\u540c\u4e3a\u81ea\u7136\u8bed\u8a00\uff09\u3002<\/section>\n

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\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u56fe 1\uff1aVML \u7684\u8bad\u7ec3\u7b97\u6cd5\u3002<\/sup><\/em><\/section>\n

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\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u56fe 2\uff1aVML \u4e2d\u6a21\u578b\u548c\u4f18\u5316\u5668<\/mark>\u7684\u81ea\u7136\u8bed\u8a00\u6a21\u7248\u6837\u4f8b\u3002<\/sup><\/em><\/section>\n
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\u56fe 1 \u663e\u793a\u4e86 VML \u7684\u5b8c\u6574\u7b97\u6cd5\u3002\u53ef\u4ee5\u770b\u89c1\u5176\u8ddf\u4f20\u7edf\u673a\u5668\u5b66\u4e60<\/mark>\u7b97\u6cd5\u57fa\u672c\u76f8\u540c\uff0c\u552f\u4e00\u7684\u533a\u522b\u662f\u6570\u636e\u548c\u53c2\u6570<\/mark>\u662f\u5728\u81ea\u7136\u8bed\u8a00\u7a7a\u95f4\u91cc\u7684\u5b57\u7b26\u4e32\uff0c \u4ee5\u53ca\u6a21\u578b<\/a>\u548c\u4f18\u5316\u5668<\/mark><\/a>\u90fd\u662f\u901a\u8fc7 LLM \u5728\u81ea\u7136\u8bed\u8a00\u7a7a\u95f4\u4e2d\u8fdb\u884c\u63a8\u7406\u3002\u56fe 2 \u4e3a\u56de\u5f52\u4efb\u52a1\u4e2d\u6a21\u578b<\/a>\u548c\u4f18\u5316\u5668<\/mark><\/a>\u7684\u5177\u4f53\u6a21\u677f\u6837\u4f8b\u3002<\/section>\n
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\u8ddf\u4f20\u7edf\u673a\u5668\u5b66\u4e60<\/mark>\u6bd4\uff0cVML \u7684\u4f18\u52bf\u5305\u62ec\uff1a\uff081\uff09\u7528\u81ea\u7136\u8bed\u8a00\u7b80\u5355\u7684\u63cf\u8ff0\u5c31\u53ef\u4ee5\u5bf9\u6a21\u578b\u52a0\u5165\u5f52\u7eb3\u504f\u7f6e (inductive bias)\uff1b\uff082\uff09\u7531\u4e8e\u4e0d\u9700\u8981\u9884\u8bbe\u6a21\u578b\u7684\u51fd\u6570\u65cf (function family)\uff0c\u4f18\u5316\u5668<\/mark><\/a>\u53ef\u4ee5\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u81ea\u52a8\u5bf9\u6a21\u578b\u7684\u51fd\u6570\u65cf\u8fdb\u884c\u9009\u62e9\uff1b\uff083\uff09\u4f18\u5316\u51fd\u6570\u5bf9\u6a21\u578b\u53c2\u6570<\/mark>\u7684\u6bcf\u4e00\u6b65\u66f4\u65b0\u90fd\u4f1a\u63d0\u4f9b\u81ea\u7136\u8bed\u8a00\u7684\u89e3\u91ca\uff0c\u540c\u65f6\u6a21\u578b\u7684\u63cf\u8ff0\u548c\u63a8\u7406\u4e5f\u662f\u81ea\u7136\u8bed\u8a00\u4e14\u53ef\u89e3\u91ca\u7684\u3002<\/section>\n
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\u5b9e\u9a8c\u5c55\u793a<\/strong><\/section>\n
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\u591a\u9879\u5f0f\u56de\u5f52<\/mark><\/strong><\/section>\n

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\u5982\u56fe 3 \u6240\u793a\uff0c\u6a21\u578b\u7684\u521d\u59cb\u53c2\u6570<\/mark><\/a>\u4e3a\u7ebf\u6027\u56de\u5f52<\/mark>\u7684\u5b9a\u4e49\u3002\u5728\u7b2c\u4e00\u6b65\u4f18\u5316\u65f6\uff0c\u4f18\u5316\u5668<\/mark>\u8bf4\u5b83\u53d1\u73b0<\/a>\u6bd4<\/a>\u6709\u66f4\u5927\u7684\u503c\u57df\uff0c\u4e14\u5b83\u4eec\u4f3c\u4e4e\u5b58\u5728\u6b63\u76f8\u5173\u6027\uff0c\u6240\u4ee5\u5b83\u51b3\u5b9a\u5c06\u6a21\u578b\u66f4\u65b0\u4e3a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52<\/mark>\u6a21\u578b\u3002<\/section>\n
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\u5728\u7b2c\u4e8c\u6b65\u4f18\u5316\u65f6\uff0c\u4f18\u5316\u5668<\/mark>\u8bf4\u5f53\u524d\u6a21\u578b\u7684\u4e0d\u826f\u8868\u73b0\u8ba9\u5b83\u610f\u8bc6\u5230\u7ebf\u6027\u6a21\u578b\u7684\u5047\u8bbe\u8fc7\u4e8e\u7b80\u5355\u4e86\uff0c\u540c\u65f6\u5b83\u53d1\u73b0<\/a>\u548c<\/a>\u4e4b\u95f4\u5b58\u5728\u975e\u7ebf\u6027\u5173\u7cfb\uff0c \u56e0\u6b64\u5b83\u51b3\u5b9a\u5c06\u6a21\u578b\u66f4\u65b0\u4e3a\u4e8c\u6b21\u51fd\u6570\u3002<\/section>\n
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\u7b2c\u4e09\u6b65\u4f18\u5316\u65f6\uff0c\u4f18\u5316\u5668<\/mark>\u7684\u5173\u6ce8\u70b9\u4ece\u51fd\u6570\u65cf\u9009\u62e9\u8f6c\u6362\u6210\u4e8c\u6b21\u51fd\u6570\u7684\u53c2\u6570<\/mark>\u4fee\u6539\u3002\u6700\u7ec8\u6a21\u578b\u5b66\u5230\u4e86\u771f\u5b9e\u51fd\u6570\u5f88\u63a5\u8fd1\u7684\u7ed3\u679c\u3002<\/section>\n

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\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u56fe 3: VML \u5728\u591a\u9879\u5f0f\u56de\u5f52<\/mark>\u4efb\u52a1\u4e2d\u7684\u8bad\u7ec3\u8fc7\u7a0b\u8bb0\u5f55\u3002<\/sup><\/em><\/section>\n
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<\/a><\/p>\n

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<\/section>\n
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<\/a><\/p>\n

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<\/p>\n
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<\/div>\n<\/div>\n<\/div>\n

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

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