{"id":45562,"date":"2025-07-11T18:00:16","date_gmt":"2025-07-11T10:00:16","guid":{"rendered":"https:\/\/case.ntu.edu.tw\/blog\/?p=45562"},"modified":"2025-07-11T14:20:18","modified_gmt":"2025-07-11T06:20:18","slug":"%e6%95%b8%e5%ad%b8%e6%87%89%e7%94%a8%e5%95%8f%e9%a1%8c%e6%80%8e%e9%ba%bc%e8%a7%a3%ef%bc%9f%e8%ae%93llm%e5%ad%b8%e6%9c%83%e4%ba%ba%e9%a1%9e%e6%80%9d%e7%b6%ad","status":"publish","type":"post","link":"https:\/\/case.ntu.edu.tw\/blog\/?p=45562","title":{"rendered":"\u6578\u5b78\u61c9\u7528\u554f\u984c\u600e\u9ebc\u89e3\uff1f\u8b93LLM\u5b78\u6703\u4eba\u985e\u601d\u7dad"},"content":{"rendered":"

\u5927\u8a9e\u8a00\u6a21\u578b\u5e36\u7d66\u4e86\u6211\u5011\u751f\u6d3b\u5f88\u591a\u7684\u4fbf\u5229\uff0c\u4e0d\u5c11\u4eba\u7684\u8cc7\u8a0a\u6aa2\u7d22\u7fd2\u6163\u5df2\u7d93\u5f9e\u641c\u5c0b\u5f15\u64ce\u8f49\u8b8a\u5230\u8a62\u554f\u5927\u8a9e\u8a00\u6a21\u578b\u3002\u4e0d\u904e\uff0c\u82e5\u662f\u78b0\u5230\u6578\u5b78\u554f\u984c\uff0c\u5c24\u5176\u662f\u6578\u5b78\u61c9\u7528\u554f\u984c\uff0c\u662f\u5426\u80fd\u8b93\u5927\u8a9e\u8a00\u6a21\u578b\u5728\u900f\u904e\u7406\u89e3\u8a9e\u8a00\u6587\u5b57\u4e4b\u5f8c\uff0c\u5b8c\u6210\u63a8\u7406\uff0c\u5217\u51fa\u6578\u5b78\u5f0f\u5b50\uff0c\u4ee5\u9032\u4e00\u6b65\u5b8c\u6210\u984c\u76ee\u7684\u8981\u6c42\u5462\uff1f<\/strong><\/span><\/p>\n

\u64b0\u6587\uff5c\u738b\u51a0\u4e91<\/strong><\/p>\n

\"\"
\u4f86\u6e90\uff1aMotionElements<\/figcaption><\/figure>\n

\u5927\u8a9e\u8a00\u6a21\u578b (Large Language Model, LLM) \u5e36\u7d66\u4e86\u6211\u5011\u751f\u6d3b\u5f88\u591a\u7684\u4fbf\u5229\uff0c\u4e0d\u5c11\u4eba\u7684\u8cc7\u8a0a\u6aa2\u7d22\u7fd2\u6163\u5df2\u7d93\u5f9e\u641c\u5c0b\u5f15\u64ce\u8f49\u8b8a\u5230\u8a62\u554f\u5927\u8a9e\u8a00\u6a21\u578b\u3002\u751a\u81f3\u5728\u6821\u5712\u5167\uff0c\u4e5f\u6709\u4e9b\u5b78\u751f\u6703\u5229\u7528\u5927\u8a9e\u8a00\u6a21\u578b\u4f86\u8907\u7fd2\u529f\u8ab2\uff0c\u6216\u8005\u5229\u7528\u5b83\u4f86\u5354\u52a9\u81ea\u5df1\u505a\u4f5c\u696d\u3002<\/p>\n

\u7136\u800c\uff0c\u5927\u8a9e\u8a00\u6a21\u578b\u7684\u672c\u8cea\u662f\u4e00\u7a2e\u8a9e\u8a00\u6a21\u578b\uff0c\u5728\u8a9e\u8a00\u6587\u5b57\u7684\u751f\u6210\u3001\u6458\u8981\u6574\u7406\u3001\u6b78\u7d0d\u7b49\u65b9\u9762\uff0c\u5177\u6709\u76f8\u7576\u8fd1\u4f3c\u65bc\u4eba\u985e\u7684\u8868\u73fe\u3002\u4e0d\u904e\uff0c\u82e5\u662f\u78b0\u5230\u6578\u5b78\u554f\u984c\uff0c\u5c24\u5176\u662f\u6578\u5b78\u61c9\u7528\u554f\u984c\uff0c\u662f\u5426\u80fd\u8b93\u5927\u8a9e\u8a00\u6a21\u578b\u5728\u900f\u904e\u7406\u89e3\u8a9e\u8a00\u6587\u5b57\u4e4b\u5f8c\uff0c\u5b8c\u6210\u63a8\u7406\uff0c\u5217\u51fa\u6578\u5b78\u5f0f\u5b50\uff0c\u4ee5\u9032\u4e00\u6b65\u5b8c\u6210\u984c\u76ee\u7684\u8981\u6c42\u5462\uff1f<\/p>\n

 <\/p>\n

\u300c\u96d9\u904e\u7a0b\u63a8\u7406\u300d\u7cfb\u7d71\u7684\u8a2d\u8a08<\/strong><\/span><\/h6>\n

\u70ba\u4e86\u6e1b\u5c11\u5927\u8a9e\u8a00\u6a21\u578b (LLM) \u5728\u89e3\u6578\u5b78\u984c\u6642\u53ef\u80fd\u6703\u51fa\u73fe\u7684\u908f\u8f2f\u932f\u8aa4\uff0c\u5728Liu\u7b49\u4eba (2025) \u7684\u7814\u7a76\u4e2d\uff0c\u5c07\u5fc3\u7406\u5b78\u7684\u300c\u96d9\u904e\u7a0b\u63a8\u7406 (Dual-Process Reasoning)\u300d\u6982\u5ff5\u52a0\u5165\u5230\u5927\u8a9e\u8a00\u6a21\u578b\u7684\u63a8\u7406\u904e\u7a0b\u4e2d\uff0c\u63d0\u51fa\u4e86\u4e00\u500b\u300cKNOS (Knowledge Guided Solver)\u300d\u6846\u67b6\u3002\u4f9d\u5faa\u8457\u300c\u96d9\u904e\u7a0b\u63a8\u7406\u300d\u7684\u7406\u8ad6\u67b6\u69cb\uff0c\u5c07\u7cfb\u7d71\u5206\u70ba\u5169\u500b\u67b6\u69cb\uff0c\u4e00\u500b\u53eb\u505a\u300c\u77e5\u8b58\u7cfb\u7d71 (knowledge system)\u300d\u3001\u53e6\u4e00\u500b\u53eb\u505a\u300c\u63a8\u65b7\u7cfb\u7d71 (inference system)\u300d\uff0c\u800c\u9019\u4e5f\u5206\u5225\u5c0d\u61c9\u5230\u300a\u5feb\u601d\u6162\u60f3\u300b\u9019\u672c\u66f8\u6240\u63d0\u5230\u7684\u7cfb\u7d71\u4e00\u4ee5\u53ca\u7cfb\u7d71\u4e8c\u5169\u7a2e\u4e0d\u540c\u985e\u578b\u7684\u601d\u8003\u7cfb\u7d71\uff0c\u5206\u5225\u4ee3\u8868\u4e86\u66f4\u6ce8\u91cd\u7d93\u9a57\u8207\u77e5\u8b58\u7684\u7cfb\u7d71\u4ee5\u53ca\u6ce8\u91cd\u61c9\u7528\u77e5\u8b58\u9032\u884c\u908f\u8f2f\u63a8\u65b7\u7684\u7cfb\u7d71\u3002<\/p>\n

\u5728\u77e5\u8b58\u7cfb\u7d71\u4e2d\uff0c\u5229\u7528\u4e86LLM\u7684\u57fa\u790e\u77e5\u8b58\uff0c\u5982\u540c\u4eba\u985e\u80fd\u900f\u904e\u7cfb\u7d71\u4e00\u9032\u884c\u77e5\u8b58\u8207\u7d93\u9a57\u7684\u53d6\u7528\u4e00\u822c\uff0c\u5728\u6b64\u7cfb\u7d71\u4e2d\uff0c\u8b93LLM\u5f97\u4ee5\u9032\u884c\u77e5\u8b58\u7684\u6fc0\u767c (invoke)\u3002\u6b64\u5916\uff0c\u6bcf\u4e00\u6b21\u7684\u77e5\u8b58\u6fc0\u767c\uff0c\u53ea\u6703\u91dd\u5c0d\u6574\u500b\u6578\u5b78\u63a8\u7406\u7684\u4e00\u500b\u6b65\u9a5f\u4f86\u9032\u884c\uff0c\u6240\u4ee5\u900f\u904eLLM\u6240\u7522\u51fa\u7684\uff0c\u6703\u662fLLM\u5728\u8655\u7406\u5b8c\u6574\u500b\u6578\u5b78\u61c9\u7528\u554f\u984c\u7684\u591a\u500b\u53e5\u5b50\u5f8c\uff0c\u6240\u5206\u5225\u5c0d\u61c9\u51fa\u4f86\u7684\u591a\u500b\u6b65\u9a5f\u7684\u96c6\u5408\u3002<\/p>\n

\u63a5\u8457\uff0c\u5728\u63a8\u65b7\u7cfb\u7d71\u4e2d\uff0c\u6703\u5c07\u62c6\u89e3\u5f8c\u7684\u984c\u76ee\u6587\u5b57\uff0c\u8f49\u63db\u6210\u9069\u7576\u7684\u7b26\u865f\u8868\u793a\uff0c\u985e\u6bd4\u65bc\u4eba\u985e\u7684\u7cfb\u7d71\u4e8c\u7684\u908f\u8f2f\u601d\u8003\uff0c\u5728\u63a8\u65b7\u7cfb\u7d71\u4e2d\u7e3d\u5171\u6709\u4e3b\u8981\u7684\u4e09\u500b\u90e8\u5206\uff0c\u5206\u5225\u70ba\u4e3b\u5e79\u89e3\u984c\u5668 (backbone solver)\u3001\u77e5\u8b58\u9a57\u8b49\u5668 (knowledge verifiere)\u3001\u77e5\u8b58\u6ce8\u5165\u5668 (knowledge injector)\u3002\u5982\u540c\u4eba\u985e\u5728\u9762\u5c0d\u554f\u984c\u6642\uff0c\u6703\u6fc0\u767c\u51fa\u904e\u53bb\u7684\u80cc\u666f\u77e5\u8b58\uff0c\u4ee5\u53ca\u8166\u4e2d\u7684\u908f\u8f2f\u63a8\u65b7\u529f\u80fd\uff0c\u5728\u77e5\u8b58\u88ab\u6fc0\u767c\u51fa\u4f86\u4e4b\u5f8c\uff0c\u4e5f\u6703\u9032\u884c\u77e5\u8b58\u7684\u8f49\u63db\uff0c\u4e26\u4e14\u5c07\u62bd\u8c61\u7684\u7b26\u865f\u300c\u6ce8\u5165\u300d\u5230LLM\u6a21\u578b\u4e2d\uff0c\u8b93LLM\u6700\u7d42\u80fd\u7522\u51fa\u9069\u7576\u7684\u6578\u5b78\u5f0f\u5b50\u3002<\/p>\n

 <\/p>\n

\u9664\u4e86\u300c\u96d9\u904e\u7a0b\u63a8\u7406\u300d\u4e4b\u5916\uff0c\u4e5f\u8b93LLM<\/strong>\u53ef\u4ee5\u300c\u958b\u66f8\u8003\u300d<\/strong><\/span><\/h6>\n

\u53e6\u5916\uff0c\u5982\u679c\u50c5\u53ea\u4f9d\u9760\u539f\u751f\u7684LLM\uff08\u5982GPT3\u6a21\u578b\u3001GPT4\u6a21\u578b\uff09\uff0c\u6216\u8005\u8b93\u539f\u751f\u7684LLM\u900f\u904e\u96d9\u904e\u7a0b\u63a8\u7406\u4f86\u9054\u6210\u89e3\u984c\uff0c\u90a3\u9ebc\u5c31\u597d\u50cf\u5b78\u6821\u7684\u300c\u9589\u5377\u8003\u300d\uff0c\u5728\u8003\u8a66\u7684\u6642\u5019\u6c92\u6709\u8fa6\u6cd5\u53c3\u770b\u5176\u4ed6\u5c0f\u6284\u3002\u56e0\u6b64\uff0cLiu\u7b49\u4eba (2025) \u4e5f\u5ef6\u4f38\u51fa\u4e86\u300cEKNOS\u300d\u6a21\u578b (External Knowledge Enhancement)\uff0c\u985e\u4f3c\u65bc\u300c\u958b\u66f8\u8003\u300d\u7684\u6982\u5ff5\uff0c\u4f7f\u7528\u77e5\u8b58\u5716\u8b5c (knowledge graph) \u5c07\u6578\u5b78\u89e3\u984c\u6703\u7528\u5230\u7684\u540d\u8a5e\u548c\u53ef\u80fd\u6703\u7528\u5230\u7684\u76f8\u5c0d\u61c9\u7684\u904b\u7b97\u9032\u884c\u77e5\u8b58\u5716\u8b5c\u7684\u9023\u7d50\uff0c\u4e26\u4e14\uff0c\u4e5f\u63d0\u4f9b\u6578\u5b78\u516c\u5f0f\u77e5\u8b58\uff0c\u4f8b\u5982\u57fa\u790e\u7684\u8ddd\u96e2\u516c\u5f0f\u3001\u7e3d\u91cd\u91cf\u3001\u7e3d\u6210\u672c\u7684\u516c\u5f0f\u7b49\u7b49\uff0c\u5e6b\u52a9LLM\u53ef\u4ee5\u66f4\u5bb9\u6613\u7684\u5f9e\u6587\u53e5\u4e2d\u64f7\u53d6\u91cd\u9ede\uff0c\u6709\u6b65\u9a5f\u7684\u8f49\u5316\u6210\u6578\u5b78\u7b26\u865f\u3002<\/p>\n

\u5728\u96d9\u904e\u7a0b\u63a8\u7406\u7684\u6d41\u7a0b\u4e2d\uff0c\u7528LLM\u4f86\u6a21\u64ec\u4e86\u4eba\u985e\u5728\u96d9\u904e\u7a0b\u63a8\u7406\u4e2d\u7d93\u6b77\u7684\u300c\u6fc0\u767c-\u9a57\u8b49\u2014\u6ce8\u5165 (invoke-verify-inject)\u300d\u7684\u6b77\u7a0b\uff0cLiu\u7b49\u4eba (2025) \u7684\u7814\u7a76\u4ee5\u4e00\u500b\u4e2d\u6587\u7684\u6578\u5b78\u984c\u76ee\u8cc7\u6599\u5eab\u548c\u4e00\u500b\u82f1\u6587\u7684\u6578\u5b78\u984c\u76ee\u8cc7\u6599\u5eab\u4f86\u9032\u884c\u6a21\u578b\u7684\u6e2c\u8a66\u8207\u9a57\u8b49\u3002\u4e26\u4e14\u9078\u5b9a\u4e86\u4e0d\u540c\u7684\u5927\u8a9e\u8a00\u6a21\u578b\u4f86\u6e2c\u8a66\u57fa\u672c\u503c (baseline)\uff0c\u4ee5\u4fbf\u65bc\u6bd4\u8f03\u4f7f\u7528\u4e86KNOS\u6846\u67b6\u6216\u662fEKNOS\u6846\u67b6\u4e4b\u5f8c\u7684\u6a21\u578b\u8868\u73fe\u3002<\/p>\n

\u7814\u7a76\u7d50\u679c\u767c\u73fe\uff0c\u4f7f\u7528\u82f1\u6587\u8cc7\u6599\u5eab\u7684\u6e2c\u8a66\u7d50\u679c\u8868\u73fe\u7565\u9ad8\u65bc\u4e2d\u6587\u8cc7\u6599\u5eab\uff0c\u539f\u56e0\u53ef\u80fd\u4f86\u81ea\u65bc\u539f\u751f\u7684LLM\u7684\u8a13\u7df4\u8cc7\u6599\u591a\u4ee5\u82f1\u6587\u70ba\u4e3b\uff0c\u56e0\u6b64\u53cd\u6620\u5728\u5be6\u969b\u6e2c\u8a66\u7684\u6548\u679c\u4e0a\u3002\u4e0d\u904e\uff0c\u6574\u9ad4\u800c\u8a00\uff0c\u52a0\u5165\u4e86KNOS\u6846\u67b6\u4ee5\u53caEKNOS\u6846\u67b6\u4e4b\u5f8c\uff0c\u6a21\u578b\u7684\u8868\u73fe\u6bd4\u8d77\u539f\u751f\u7684LLM\u6548\u679c\u66f4\u597d\uff0c\u800c\u4e14\u4f7f\u7528EKNOS\u7684\u8a71\uff0c\u7531\u65bc\u8b93LLM\u5f97\u4ee5\u5728\u63a8\u7406\u7684\u540c\u6642\u53c8\u6709\u300c\u5c0f\u6284\u300d\u53ef\u4ee5\u53c3\u8003\uff0c\u56de\u7b54\u554f\u984c\u7684\u6b63\u78ba\u7387\u53c8\u7565\u9ad8\u65bcKNOS\u6846\u67b6\u3002<\/p>\n

\u6b64\u5916\uff0cLiu\u7b49\u4eba (2025) \u4e5f\u5728\u7814\u7a76\u4e2d\u63d0\u5230\u4e86\u4f7f\u7528\u6a21\u578b\u7684\u6548\u7387\u4ee5\u53ca\u6210\u672c\u8003\u91cf\uff0c\u5728\u8f03\u56f0\u96e3\u7684\u63a8\u7406\u60c5\u5883\u6216\u8005\u662f\u8f03\u591a\u63a8\u7406\u6b65\u9a5f\u7684\u60c5\u5883\u4e4b\u4e0b\uff0c\u4f7f\u7528EKNOS\u6846\u67b6\u7684\u8a71\uff0c\u53ef\u4ee5\u518d\u56f0\u96e3\u53ca\u8907\u96dc\u4efb\u52d9\u4e2d\uff0c\u4ecd\u7136\u4e0d\u81f3\u65bc\u8b93\u6a21\u578b\u7684\u8868\u73fe\u964d\u4f4e\u592a\u591a\uff0c\u5e45\u5ea6\u7686\u6bd4\u539f\u751fLLM\u9084\u5c0f\uff0c\u800c\u4e14\u5728\u8655\u7406\u5c0d\u65bcLLM\u4f86\u8aaa\u300c\u4e0d\u662f\u6bcd\u8a9e\u300d\u7684\u4e2d\u6587\u8cc7\u6599\u5eab\u65b9\u9762\uff0c\u4e5f\u80fd\u7dad\u6301\u8f03\u597d\u7684\u8868\u73fe\u3002\u82e5\u8ad6\u53ca\u6a21\u578b\u4f7f\u7528\u7684LLM API\u6578\u91cf\uff0c\u4f7f\u7528EKNOS\u7684\u89e3\u6cd5\u4e5f\u6bd4\u8d77\u5176\u4ed6\u985e\u578b\u7684\u6846\u67b6\u9084\u8981\u66f4\u80fd\u7bc0\u7701API\u7684\u547c\u53eb\u6b21\u6578\u3002\uff08API\u547c\u53eb\u6b21\u6578\u8d8a\u4f4e\uff0c\u8d8a\u7701\u6210\u672c\uff0c\u56e0\u70ba\u53ef\u4ee5\u7701\u53bb\u6bcf\u6b21\u547c\u53eb\u6240\u9700\u8ca0\u64d4\u7684\u8cbb\u7528\uff09<\/p>\n

 <\/p>\n

\u904b\u7528\u63d0\u793a\u5de5\u7a0b\u8207\u5fc3\u7406\u5b78\uff0c\u63d0\u5347LLM\u6548\u80fd<\/strong><\/span><\/h6>\n

\u7e3d\u7d50\u4f86\u8aaa\uff0cLiu\u7b49\u4eba (2025) \u7684\u7814\u7a76\u900f\u904e\u5f9e\u5fc3\u7406\u5b78\u4e0a\u63d0\u4f9b\u7684\u7406\u8ad6\u9032\u884c\u767c\u60f3\uff0c\u8a2d\u8a08\u51fa\u53ef\u4ee5\u8b93LLM\u5b78\u7fd2\u4e26\u4e14\u6a21\u64ec\u4eba\u985e\u5fc3\u667a\u904b\u4f5c\u7684\u63d0\u793a\u8a5e (prompt)\u3002\u540c\u6642\uff0c\u4e5f\u7d50\u5408\u4e86\u4e00\u4e9b\u8cc7\u8a0a\u7cfb\u7d71\u8a2d\u8a08\u7684\u512a\u52e2\uff0c\u4f8b\u5982\uff0c\u900f\u904e\u6a39\u72c0\u7d50\u69cb\u8a2d\u8a08\u77e5\u8b58\u5716\u8b5c\uff0c\u8b93\u6a21\u578b\u80fd\u5920\u66f4\u6709\u6548\u7684\u63d0\u53d6\u76f8\u95dc\u77e5\u8b58\u4f86\u9032\u884c\u908f\u8f2f\u63a8\u7406\u7b49\u7b49\u3002\u800c\u5728\u7814\u7a76\u4e2d\uff0c\u4e5f\u9a57\u8b49\u4e86\u6210\u672c\u6548\u7387\u65b9\u9762\uff0c\u80fd\u56e0\u6b64\u800c\u964d\u4f4e\u8cbb\u7528\u7684\u6d88\u8017\u3002<\/p>\n

\u7814\u7a76\u8005\u5728\u8ad6\u6587\u4e2d\u63d0\u5230\uff0c\u672a\u4f86\u5e0c\u671b\u53ef\u4ee5\u7d50\u5408\u5176\u4ed6\u4eba\u985e\u63a8\u7406\u7684\u884c\u70ba\u9032\u5165\u5230\u6a21\u578b\u7684\u6846\u67b6\u4e4b\u4e2d\uff0c\u4f8b\u5982\u4eba\u985e\u7684\u8a08\u756b\u8207\u53cd\u601d\u7684\u884c\u70ba\u3002\u53e6\u5916\uff0c\u4e5f\u5e0c\u671b\u53ef\u4ee5\u4f7f\u7528\u66f4\u524d\u7aef\u7684LLM\u6a21\u578b\u6216\u8005\u9032\u884c\u5176\u4ed6\u985e\u578b\u7684\u6578\u5b78\u984c\u76ee\u7684\u89e3\u984c\u6e2c\u8a66\u3002\u800c\u672c\u7814\u7a76\u5617\u8a66\u6a21\u64ec\u4e86\u4eba\u985e\u7684\u8a8d\u77e5\u884c\u70ba\uff0c\u5e0c\u671b\u80fd\u4ee5\u6b64\u7814\u7a76\u62cb\u8f49\u5f15\u7389\uff0c\u5c0d\u5176\u4ed6\u7814\u7a76\u4e5f\u80fd\u6709\u6240\u8ca2\u737b\u3002<\/p>\n

 <\/p>\n

\n

\u53c3\u8003\u6587\u737b<\/strong><\/p>\n

    \n
  1. Liu, Z. Huang, Q. Liu, Z. Ma, C. Zhai and E. Chen, \"Knowledge-Centered Dual-Process Reasoning for Math Word Problems With Large Language Models,\" in IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 6, pp. 3457-3471, June 2025, doi: 10.1109\/TKDE.2025.3556367.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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