{"id":51220,"date":"2024-07-04T21:01:33","date_gmt":"2024-07-04T13:01:33","guid":{"rendered":"https:\/\/swarma.org\/?p=51220"},"modified":"2024-07-04T21:01:33","modified_gmt":"2024-07-04T13:01:33","slug":"%e5%9b%a0%e6%9e%9c%e7%a7%91%e5%ad%a6%e5%a4%a7%e6%a8%a1%e5%9e%8b%e7%a0%94%e7%a9%b6%e5%89%8d%e6%b2%bf%e6%a6%82%e8%bf%b0%e4%b8%8e%e5%b1%95%e6%9c%9b-%e5%91%a8%e6%97%a5%e7%9b%b4%e6%92%ad","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=51220","title":{"rendered":"\u56e0\u679c\u79d1\u5b66+\u5927\u6a21\u578b\u7814\u7a76\u524d\u6cbf\u6982\u8ff0\u4e0e\u5c55\u671b | \u5468\u65e5\u76f4\u64ad"},"content":{"rendered":"
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Felix Leeb\uff1a\u5927\u6a21\u578b\u65f6\u4ee3\u4e0b\uff0c\u4e3a\u4ec0\u4e48\u4ecd\u9700\u8981\u56e0\u679c\u79d1\u5b66\uff1f<\/span><\/strong><\/span><\/h2>\n

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\u66f4\u91cd\u8981\u7684\u662f\uff0c\u8fd9\u90e8\u5206\u7684\u7814\u7a76\u53ef\u4ee5\u6269\u5c55\u5230\u793e\u4f1a\u79d1\u5b66\u9886\u57df\uff0c\u7814\u7a76\u793e\u4f1a\u73b0\u8c61\u4e2d\u7684\u56e0\u679c\u5173\u7cfb\uff0c\u5982\u653f\u7b56\u5f71\u54cd\u5206\u6790\u548c\u6027\u522b\u504f\u89c1\u6d4b\u91cf\u3002\u6211\u4eec\u901a\u8fc7\u786e\u4fddLLMs\u7684\u53ef\u9760\u6027\u5e76\u5728\u591a\u79cd\u793e\u4f1a\u5e94\u7528\u4e2d\u53d1\u6325\u5176\u5f71\u54cd\u529b\uff0c\u4e3a\u5b9e\u73b0\u8d1f\u8d23\u4efb\u7684AI\u7cfb\u7edf\u63d0\u4f9b\u4e86\u4e00\u6761\u660e\u786e\u7684\u8def\u7ebf\u56fe\u3002<\/span><\/section>\n


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\u6768\u68a6\u6708\uff1a\u56e0\u679c\u63a8\u7406\u5982\u4f55\u8d4b\u80fd\u5927\u8bed\u8a00\u6a21\u578b\u548cAI Agent\uff1f<\/span><\/strong><\/span><\/h2>\n

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\u5728\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLMs\uff09\u4e2d\uff0c\u4ee3\u7406\uff08Agent\uff09\u7684\u51b3\u7b56\u901a\u5e38\u901a\u8fc7\u4e09\u79cd\u65b9\u5f0f\u8fdb\u884c\u653f\u7b56\u5b66\u4e60\uff1a\u5229\u7528\u4e13\u5bb6\u6570\u636e\u7684\u76d1\u7763\u5fae\u8c03\uff08SFT\uff09\uff0c\u4e0a\u4e0b\u6587\u5b66\u4e60\uff08ICL\uff09\u548c\u5f3a\u5316\u5b66\u4e60\uff08RL\uff09\u3002\u7136\u800c\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u7f3a\u4e4f\u53ef\u89e3\u91ca\u6027\uff0c\u5bfc\u81f4\u5bf9\u4e16\u754c\u548c\u7cfb\u7edf\u7684\u7406\u89e3\u4ea7\u751f\u4e0d\u786e\u5b9a\u6027\uff0c\u4ece\u800c\u5bfc\u81f4\u51b3\u7b56\u7684\u65b9\u5411\u53d8\u5f97\u4e0d\u53d7\u63a7\u5236\u6216\u4f4e\u6548\u3002\u56e0\u679c\u63a8\u7406\u4f5c\u4e3a\u4e00\u79cd\u7528\u4e8e\u89e3\u91ca\u6027\u5206\u6790\u7684\u5f3a\u5927\u5efa\u6a21\u5de5\u5177\uff0c\u901a\u8fc7\u5206\u6790\u4e8b\u4ef6\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\uff0c\u4f7f\u4eba\u4eec\u80fd\u591f\u66f4\u597d\u5730\u7406\u89e3\u4e8b\u7269\u53d1\u5c55\u7684\u89c4\u5f8b\uff0c\u4ece\u800c\u5e2e\u52a9LLMs\u7684\u51b3\u7b56\u8fc7\u7a0b\u66f4\u52a0\u903b\u8f91\u5316\u548c\u4e13\u6ce8\u4e8e\u56e0\u679c\u6548\u5e94\uff0c\u63d0\u9ad8\u7cfb\u7edf\u7684\u53ef\u89e3\u91ca\u6027\u548c\u7a33\u5b9a\u6027\u3002\u5728\u672c\u8bb2\u5ea7\u4e2d\uff0c\u6211\u5c06\u4ecb\u7ecdLLMs\u548c\u4ee3\u7406\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u4ee5\u53ca\u6709\u5173\u56e0\u679c\u63a8\u7406\u548c\u56e0\u679c\u5efa\u6a21\u5728\u589e\u5f3a\u4ee3\u7406\u51b3\u7b56\u80fd\u529b\u65b9\u9762\u7684\u4e00\u7cfb\u5217\u7814\u7a76\u5de5\u4f5c\u3002\u901a\u8fc7\u8fd9\u4e9b\u7814\u7a76\uff0c\u6211\u4eec\u65e8\u5728\u63d0\u9ad8\u4ee3\u7406\u7684\u51b3\u7b56\u53ef\u89e3\u91ca\u6027\u548c\u6548\u7387\uff0c\u5b9e\u73b0\u66f4\u53ef\u9760\u548c\u53ef\u63a7\u5236\u7684\u51b3\u7b56\u8fc7\u7a0b\u3002<\/span><\/section>\n


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Felix Leeb<\/span><\/strong> \u662f\u5fb7\u56fd\u667a\u80fd\u7cfb\u7edf\u9a6c\u514b\u65af\u00b7\u666e\u6717\u514b\u7814\u7a76\u6240\u7684\u535a\u58eb\u7814\u7a76\u751f\uff0c\u5728Bernhard Sch\u00f6lkopf\u7684\u6307\u5bfc\u4e0b\uff0c\u81f4\u529b\u4e8e\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u548c\u8bc4\u4f30\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\u3002\u5148\u524d\uff0cFelix Leeb \u5728\u7f8e\u56fd\u897f\u96c5\u56fe\u7684\u534e\u76db\u987f\u5927\u5b66\u5b8c\u6210\u4e86\u7269\u7406\u3001\u5316\u5b66\u548c\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u5b66\u58eb\u5b66\u4f4d\uff0c\u5e76\u8fdb\u884c\u4e86\u5173\u4e8e\u7269\u4f53\u59ff\u52bf\u4f30\u8ba1\u89c6\u89c9\u8fd0\u52a8\u63a7\u5236\u3001\u4f7f\u7528\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u7814\u7a76\u7ec6\u80de\u819c\u7ed3\u6784\u7b49\u7814\u7a76\u3002<\/span><\/section>\n

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\u91d1\u81f4\u9759<\/strong>\u535a\u58eb\u5373\u5c06\u4e8e2025\u5e74\u590f\u5b63\u52a0\u5165\u591a\u4f26\u591a\u5927\u5b66\uff08University of Toronto\uff09\u8ba1\u7b97\u673a\u79d1\u5b66\u7cfb\uff0c\u62c5\u4efb\u52a9\u7406\u6559\u6388\u3002<\/span>\u5979\u73b0\u5728\u662f\u9a6c\u514b\u65af\u00b7\u666e\u6717\u514b\u7814\u7a76\u6240\u535a\u58eb\u751f\uff0c\u7531 \u9a6c\u514b\u65af\u666e\u6717\u514b\u7814\u7a76\u6240 \u548c\u82cf\u9ece\u4e16\u8054\u90a6\u7406\u5de5\u5b66\u9662 \u7684 Bernhard Schoelkopf\u6307\u5bfc\uff0c\u7531Rada Mihalcea (\u5bc6\u6b47\u6839\u5927\u5b66)\u6307\u5bfc \uff0c\u5e76\u7531\u82cf\u9ece\u4e16\u8054\u90a6\u7406\u5de5\u5b66\u9662\u7684Mrinmaya Sachan\u5171\u540c\u6307\u5bfc \u3002\u5979\u7684\u7814\u7a76\u91cd\u70b9\u662f\u901a\u8fc7\u56e0\u679c\u63a8\u7406\u5b9e\u73b0\u5bf9\u793e\u4f1a\u8d1f\u8d23\u7684 NLP\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5206\u4e3a\u4ee5\u4e0b\u4e24\u4e2a\u90e8\u5206\uff1a(1) \u63a8\u52a8 NLP \u9020\u798f\u793e\u4f1a\uff1b(2) \u5f00\u53d1CausalNLP\uff0c\u4ee5<\/span>\u63d0\u9ad8 NLP \u6a21\u578b\u7684\u7a33\u5065\u6027\u3001\u516c\u5e73\u6027\u548c\u53ef\u89e3\u91ca\u6027\uff0c\u5e76\u5206\u6790\u793e\u4f1a\u95ee\u9898\u7684\u539f\u56e0\u3002<\/span><\/section>\n

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\u6768\u68a6\u6708<\/strong>\u535a\u58eb\u5373\u5c06\u4e8e2024\u5e74\u5e95\u52a0\u5165\u5e03\u91cc\u65af\u6258\u5927\u5b66\uff08University of Bristol\uff09\u62c5\u4efb\u52a9\u7406\u6559\u6388\u3002\u5979\u73b0\u5728\u662f\u4f26\u6566\u5927\u5b66\u5b66\u9662\u8ba1\u7b97\u673a\u535a\u58eb\u751f\uff0c\u5bfc\u5e08\u4e3a\u6c6a\u519b\u6559\u6388\u3002\u4e3b\u8981\u7814\u7a76\u5174\u8da3\u4e3a\u56e0\u679c\u8868\u5f81\u5b66\u4e60\uff0c\u591a\u667a\u80fd\u4f53\uff0c\u5f3a\u5316\u5b66\u4e60\u7b49\u3002\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u4e3a\u57fa\u4e8e\u56e0\u679c\u8868\u793a\u7684\u51b3\u7b56\u7cfb\u7edf\u3002\u535a\u58eb\u671f\u95f4\u5728\u4eba\u5de5\u667a\u80fd\u9876\u7ea7\u4f1a\u8bae\u671f\u520a NeurIPS\uff0cCVPR\uff0cKDD\uff0cSIGIR\uff0cWWW\uff0cACM TOIS\u7b49\u53d1\u8868\u82e5\u5e72\u7814\u7a76\u6210\u679c\uff0c\u5e76\u88abKAUST\u8bc4\u9009\u4e3aRising Star in AI\u3002\u76ee\u524d\u62c5\u4efbNeurIPS\uff0cICML\uff0cICLR\uff0cKDD\uff0cTNNLS\u7b49\u4f1a\u8bae\u671f\u520a\u7684PC member\u6216\u5ba1\u7a3f\u4eba\u3002<\/span><\/section>\n
\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/ymy4323460.github.io\/<\/span><\/section>\n

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\u674e\u660a\u8f69<\/span><\/strong>\uff0c \u5317\u4eac\u5927\u5b66\u5927\u6570\u636e\u79d1\u5b66\u7814\u7a76\u4e2d\u5fc3\uff0c\u6570\u636e\u79d1\u5b66\uff08\u7edf\u8ba1\u5b66\uff09\u535a\u58eb\uff0cCCF\u4f1a\u5458\u3001IEEE\u4f1a\u5458\u3001ACM\u4f1a\u5458\u3002\u7814\u7a76\u5174\u8da3\u4e3a\u56e0\u679c\u673a\u5668\u5b66\u4e60\u7406\u8bba\u3001\u53cd\u4e8b\u5b9e\u516c\u5e73\u6027\u3001\u63a8\u8350\u7cfb\u7edf\u53bb\u504f\u3001\u5206\u5e03\u5916\u6cdb\u5316\u3001\u591a\u6e90\u6570\u636e\u878d\u5408\u3001\u751f\u7269\u4fe1\u606f\u5b66\u548c\u5927\u8bed\u8a00\u6a21\u578b\u7b49\u3002\u5df2\u5728ICML\u3001NeurIPS\u3001ICLR\u3001KDD\u3001WWW\u3001AAAI\u3001IJCAI\u7b49\u591a\u4e2aCCF-A\u9876\u5c16\u4f1a\u8bae\u4ee5\u7b2c\u4e00\u4f5c\u8005\u53d1\u8868\u591a\u7bc7\u8bba\u6587\uff0c\u5176\u4e2d5\u7bc7\u8bba\u6587\u88ab\u8bc4\u9009\u4e3aSpotlight\u6216Oral\uff0c\u73b0\u4e3aICML\u3001NeurIPS\u3001ICLR\u3001KDD\u3001WWW\u3001AAAI\u3001IJCAI\u7b49\u591a\u4e2a\u9876\u4f1aPC member\u6216Area Chair\uff0c\u4ee5\u53caTKDE\u3001TOIS\u3001TKDD\u3001The Innovation\u3001\u300a\u4e2d\u56fd\u79d1\u5b66\uff1a\u4fe1\u606f\u79d1\u5b66\u300b\u7b49\u591a\u4e2a\u9876\u7ea7\u671f\u520a\u5ba1\u7a3f\u4eba\uff0c14\u9879\u53d1\u660e\u4e13\u5229\u3002\u8fde\u7eed\u4e24\u5e74\u83b7\u5f97\u5317\u4eac\u5927\u5b66\u535a\u58eb\u6700\u9ad8\u7814\u7a76\u5956\u201c\u6821\u957f\u5956\u5b66\u91d1\u201d\uff0c\u83b7\u56fd\u5bb6\u5956\u5b66\u91d1\uff0c\u4e5d\u5764\uff08\u4eba\u5de5\u667a\u80fd\u65b9\u5411\uff09\u5956\u5b66\u91d1\uff0c\u5317\u4eac\u5927\u5b66\u4e09\u597d\u5b66\u751f\uff0c\u4e24\u9879\u6210\u679c\u83b7\u5317\u4eac\u5927\u5b66\u201c\u6311\u6218\u676f\u201d\u4e94\u56db\u9752\u5e74\u79d1\u5b66\u5956\u7279\u7b49\u5956\uff0c\u5e76\u83b7\u5f97\u9996\u6279\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u9752\u5e74\u5b66\u751f\u57fa\u7840\u7814\u7a76\u9879\u76ee\uff08\u535a\u58eb\u7814\u7a76\u751f\uff0930\u4e07\u8d44\u52a9\u3002<\/span><\/section>\n


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7\u67087\u65e520:30-21:30\uff08\u5468\u65e5\uff09\uff0c\u76f4\u64ad\u62a5\u540d\u5165\u53e3\u89c1\u540e\u6587\u3002<\/span><\/section>\n
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\u626b\u7801\u9884\u7ea6\u672c\u6b21\u76f4\u64ad<\/span><\/section>\n

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\u65f6\u95f4\u5b89\u6392\uff1a<\/span><\/strong><\/h3>\n
\u8bf4\u660e\uff1aFelix Leeb\u7684\u65f6\u5dee\u95ee\u9898\uff0c\u6211\u4eec\u8c03\u6574\u5230\u4e86\u6700\u540e\u4e00\u4e2a\u5206\u4eab\u3002<\/span><\/section>\n\n\n\n\n<\/colgroup>\n\n\n\n\n\n
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[1] Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, and Furong Huang. Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey, March 2024.<\/span><\/p>\n

[2]  Linying Yang, Oscar Clivio, Vik Shirvaikar, and Fabian Falck. A critical review of causal inference benchmarks for large language models. In AAAI 2024 Workshop on\u201dAre Large Language Models Simply Causal Parrots?\u201d<\/em>, 2023.<\/span><\/p>\n

[3]  Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, et al. Foundational challenges in assuring alignment and safety of large language models. arXiv preprint arXiv:2404.09932<\/em>, 2024.<\/span><\/p>\n

[4]  Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez Adauto, Max Kleiman-Weiner, Mrinmaya Sachan, et al. CLadder: A benchmark to assess causal reasoning capabilities of language models. Advances in Neural Information Processing Systems<\/em>, 36, 2024.<\/span><\/p>\n

\u91d1\u81f4\u9759\u8001\u5e08\u6574\u4f53\u7684\u5173\u4e8eTutorial\uff1a<\/span><\/section>\n
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\u201c\u56e0\u679c\u201d\u5e76\u4e0d\u662f\u4e00\u4e2a\u65b0\u6982\u5ff5\uff0c\u800c\u662f\u4e00\u4e2a\u5df2\u7ecf\u5728\u591a\u4e2a\u5b66\u79d1\u4e2d\u4f7f\u7528\u4e86\u6570\u5341\u5e74\u7684\u5206\u6790\u6280\u672f\u3002\u96c6\u667a\u4ff1\u4e50\u90e8\u5728\u8fc7\u53bb4\u5e74\u671f\u95f4\u56f4\u7ed5\u7814\u7a76\u4eba\u5458\u7684\u4e0d\u540c\u89d2\u5ea6\u7684\u9700\u6c42\uff0c\u4e3e\u529e\u4e864\u5b63\u76f8\u5173\u4e3b\u9898\u7684\u8bfb\u4e66\u4f1a\uff0c\u5f62\u6210\u4e86\u6570\u5343\u4eba\u89c4\u6a21\u7684\u793e\u533a\u3002<\/span><\/section>\n

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\u3010<\/span>\u7b2c\u4e00\u5b63\uff1a\u56e0\u679c\u79d1\u5b66\u4e0eCausal AI<\/span><\/a>\u3011\u57fa\u4e8e\u300aElements of Causal Inference\u300b\uff0c\u63a2\u8ba8\u56e0\u679c\u79d1\u5b66\u5728\u673a\u5668\u5b66\u4e60\u65b9\u9762\u7684\u5e94\u7528\uff0c\u5982\u5f3a\u5316\u5b66\u4e60\u548c\u8fc1\u79fb\u5b66\u4e60\u7b49\uff0c\u5e76\u5206\u4eab\u5de5\u4e1a\u5e94\u7528\u3002<\/span><\/span><\/p>\n

\u3010<\/span>\u7b2c\u4e8c\u5b63\uff1a\u56e0\u679c\u79d1\u5b66\u4e0e\u57fa\u7840\u5b9e\u6218<\/span><\/a>\u3011\u805a\u7126\u5b9e\u64cd\u548c\u57fa\u7840\uff0c\u6df1\u5165\u5b66\u4e60\u300aCausal inference in statistics: A primer\u300b\u548c\u300aElements of causal inference: foundations and learning algorithms\u300b\u3002 <\/span><\/span><\/p>\n

\u3010<\/span>\u7b2c\u4e09\u5b63\uff1a\u56e0\u679c\u79d1\u5b66\u4e0eCausal +X<\/span><\/a>\u3011\u56de\u987e\u793e\u4f1a\u5b66\u3001\u7ecf\u6d4e\u5b66\u3001\u533b\u5b66\uff0c\u8ba1\u7b97\u673a\u7b49\u9886\u57df\u7684\u56e0\u679c\u6a21\u578b\u548c\u8303\u5f0f\uff0c\u5c1d\u8bd5\u7528\u73b0\u4ee3\u6a21\u578b\u63d0\u4f9b\u65b0\u601d\u8def\u3002 <\/span><\/span><\/p>\n

\u3010<\/span>\u7b2c\u56db\u5b63\uff1a\u56e0\u679c\u8868\u5f81\u5b66\u4e60<\/span><\/a>\u3011\u63a2\u8ba8\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u7684\u7406\u8bba\u3001\u6280\u672f\u548c\u6700\u65b0\u5e94\u7528\uff0c\u6d89\u53ca\u56e0\u679c\u751f\u6210\u6a21\u578b\u3001\u53ef\u89e3\u91ca\u6027\u3001\u516c\u5e73\u6027\u53ca\u5de5\u4e1a\u843d\u5730\u3002<\/span><\/span><\/p>\n

\u7b2c\u4e94\u5b63\u8bfb\u4e66\u4f1a<\/strong><\/span><\/a>\u4e3b\u8981\u56f4\u7ed5\u56e0\u679c\u79d1\u5b66\u7684\u6700\u65b0\u8fdb\u5c55\uff0c\u5305\u62ec\u56e0\u679c\u79d1\u5b66\u4e0e\u5927\u6a21\u578b\u7684\u7ed3\u5408\u7b49\u65b9\u9762\u8fdb\u884c\u6df1\u5ea6\u7684\u63a2\u8ba8\u548c\u68b3\u7406\uff0c\u5e0c\u671b\u7ed9\u5728\u8fd9\u4e2a\u9886\u57df\u7684\u7814\u7a76\u8005\u63d0\u4f9b\u4e00\u4e2a\u5168\u9762\u7684\u7814\u7a76\u56fe\u666f\u3002\u5171\u540c\u63a2\u8ba8\u56e0\u679c\u79d1\u5b66\u7684\u672a\u6765\u53d1\u5c55\u4ee5\u53ca\u9762\u4e34\u7684\u6311\u6218\u3002<\/span><\/section>\n

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