{"id":35120,"date":"2024-12-19T00:59:50","date_gmt":"2024-12-18T16:59:50","guid":{"rendered":"https:\/\/17aitech.com\/?p=35120"},"modified":"2024-12-19T00:59:50","modified_gmt":"2024-12-18T16:59:50","slug":"acl-2024%e5%a5%96%e9%a1%b9%e5%85%ac%e5%b8%83%ef%bc%9a%e5%8d%8e%e7%a7%91%e5%a4%a7%e7%a0%b4%e8%af%91%e7%94%b2%e9%aa%a8%e6%96%87%e6%9c%80%e4%bd%b3%e8%ae%ba%e6%96%87%e4%b9%8b%e4%b8%80%e3%80%81glove","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=35120","title":{"rendered":"ACL 2024\u5956\u9879\u516c\u5e03\uff1a\u534e\u79d1\u5927\u7834\u8bd1\u7532\u9aa8\u6587\u6700\u4f73\u8bba\u6587\u4e4b\u4e00\u3001GloVe\u65f6\u95f4\u68c0\u9a8c\u5956"},"content":{"rendered":"<p>\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:<a href=\"https:\/\/www.jiqizhixin.com\/articles\/2024-08-15-4\" target=\"_blank\">ACL 2024\u5956\u9879\u516c\u5e03\uff1a\u534e\u79d1\u5927\u7834\u8bd1\u7532\u9aa8\u6587\u6700\u4f73\u8bba\u6587\u4e4b\u4e00\u3001GloVe\u65f6\u95f4\u68c0\u9a8c\u5956<\/a><\/p>\n<blockquote data-author-name=\"\" data-content-utf8-length=\"19\" data-source-title=\"\" data-type=\"2\" data-url=\"\">\n<section>\n<section>\n<section>\u672c\u5c4a ACL \u5927\u4f1a\uff0c\u6295\u7a3f\u8005\u300c\u6536\u83b7\u6ee1\u6ee1\u300d\u3002<\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n<section><\/section>\n<section>\u4e3a\u671f\u516d\u5929\u7684 ACL 2024 \u6b63\u5728\u6cf0\u56fd\u66fc\u8c37\u4e3e\u529e\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-69b7cea14ee0000b63b96b3398b23a96.jpeg\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-69b7cea14ee0000b63b96b3398b23a96.jpeg\"><\/a><\/p>\n<section>ACL \u662f\u8ba1\u7b97<mark data-type=\"concepts\" data-id=\"e495445a-c189-4783-b0fb-17dcd3bfc9de\">\u8bed\u8a00\u5b66<\/mark>\u548c<mark data-type=\"tech_tasks\" data-id=\"c8ff5114-6cbb-49ca-8a89-3ee2826be0b4\">\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/mark>\u9886\u57df\u7684\u9876\u7ea7\u56fd\u9645\u4f1a\u8bae\uff0c\u7531\u56fd\u9645\u8ba1\u7b97<mark data-type=\"concepts\" data-id=\"e495445a-c189-4783-b0fb-17dcd3bfc9de\">\u8bed\u8a00\u5b66<\/mark>\u534f\u4f1a\u7ec4\u7ec7\uff0c\u6bcf\u5e74\u4e3e\u529e\u4e00\u6b21\u3002\u4e00\u76f4\u4ee5\u6765\uff0cACL \u5728 NLP \u9886\u57df\u7684\u5b66\u672f\u5f71\u54cd\u529b\u90fd\u4f4d\u5217\u7b2c\u4e00\uff0c\u5b83\u4e5f\u662f CCF-A \u7c7b\u63a8\u8350\u4f1a\u8bae\u3002<\/section>\n<section><\/section>\n<section>\u4eca\u5e74\u7684 ACL \u5927\u4f1a\u5df2\u662f\u7b2c 62 \u5c4a\uff0c\u63a5\u6536\u4e86 400 \u4f59\u7bc7 NLP \u9886\u57df\u7684\u524d\u6cbf\u5de5\u4f5c\u3002\u6628\u5929\u4e0b\u5348\uff0c\u5927\u4f1a\u516c\u5e03\u4e86\u6700\u4f73\u8bba\u6587\u7b49\u5956\u9879\u3002\u6b64\u6b21\uff0c\u6700\u4f73\u8bba\u6587\u5956 7 \u7bc7\uff08\u4e24\u7bc7\u672a\u516c\u5f00\uff09\u3001\u6700\u4f73\u4e3b\u9898\u8bba\u6587\u5956 1 \u7bc7\u3001\u6770\u51fa\u8bba\u6587\u5956 35 \u7bc7\u3002<\/section>\n<section><\/section>\n<section>\u5927\u4f1a\u8fd8\u8bc4\u51fa\u4e86\u8d44\u6e90\u8bba\u6587\u5956\uff08Resource Award\uff093 \u7bc7\u3001\u793e\u4f1a\u5f71\u54cd\u529b\u5956\uff08Social Impact Award\uff093 \u7bc7\u3001\u65f6\u95f4\u68c0\u9a8c\u5956 2 \u7bc7\u3002<\/section>\n<section><\/section>\n<section>\u6b64\u5916\uff0c\u672c\u5c4a\u5927\u4f1a\u7ec8\u8eab\u6210\u5c31\u5956\u9881\u7ed9\u4e86\u7ebd\u7ea6\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u7cfb\u6559\u6388 Ralph Grishman\u3002<\/section>\n<section><\/section>\n<section>\u4ee5\u4e0b\u662f\u5177\u4f53\u7684\u83b7\u5956\u4fe1\u606f\u3002<\/section>\n<section><\/section>\n<section><strong>\u6700\u4f73\u8bba\u6587<\/strong><\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-532bf71bb88c9ea1e78004817f5133b1.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-532bf71bb88c9ea1e78004817f5133b1.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 1\uff1aMission: Impossible Language Models<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aJulie Kallini, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, Christopher Potts<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u65af\u5766\u798f\u5927\u5b66\u3001\u52a0\u5dde\u5927\u5b66\u5c14\u6e7e\u5206\u6821\u3001\u5f97\u514b\u8428\u65af\u5927\u5b66\u5965\u65af\u6c40\u5206\u6821<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2401.06416<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u8bba\u6587\u7b80\u4ecb\uff1a\u4e54\u59c6\u65af\u57fa\u7b49\u4eba\u8ba4\u4e3a\uff1a\u5bf9\u4e8e\u4eba\u7c7b\u53ef\u80fd\u6216\u4e0d\u53ef\u80fd\u5b66\u4f1a\u7684\u8bed\u8a00\uff0c\u5927\u578b<mark data-type=\"tech_tasks\" data-id=\"bf35ef94-d956-4033-a533-0c0828308c36\">\u8bed\u8a00\u6a21\u578b<\/mark>\uff08LLM\uff09\u7684\u5b66\u4e60\u80fd\u529b\u662f\u4e00\u6837\u7684\u3002\u7136\u800c\uff0c\u51e0\u4e4e\u6ca1\u6709\u516c\u5f00\u7684\u5b9e\u9a8c\u8bc1\u636e\u6765\u652f\u6301\u8fd9\u79cd\u8bf4\u6cd5\u3002<\/section>\n<section><\/section>\n<section>\u8be5\u7814\u7a76\u5f00\u53d1\u4e86\u4e00\u7ec4\u5177\u6709\u4e0d\u540c\u590d\u6742\u6027\u7684\u5408\u6210\u8bed\u8a00\uff0c\u6bcf\u4e00\u79cd\u90fd\u662f\u901a\u8fc7\u4f7f\u7528\u4e0d\u81ea\u7136\u7684\u8bcd\u5e8f\u548c\u8bed\u6cd5\u89c4\u5219\u7cfb\u7edf\u5730\u6539\u53d8\u82f1\u8bed\u6570\u636e\u800c\u8bbe\u8ba1\u7684\uff0c\u65e8\u5728\u5408\u6210\u4eba\u7c7b\u4e0d\u53ef\u80fd\u5b66\u4f1a\u7684\u8bed\u8a00\u3002<\/section>\n<section><\/section>\n<section>\u8be5\u7814\u7a76\u8fdb\u884c\u4e86\u5e7f\u6cdb\u7684\u8bc4\u4f30\u5b9e\u9a8c\uff0c\u4ee5\u8bc4\u4f30 <mark data-type=\"tech_methods\" data-id=\"e8e4513e-695d-49e6-9155-31ae453de372\">GPT-2<\/mark> \u5c0f\u6a21\u578b\u5b66\u4e60\u8fd9\u4e9b\u300c\u4e0d\u53ef\u80fd\u8bed\u8a00\u300d\u7684\u80fd\u529b\uff0c\u5e76\u4e14\u5728\u6574\u4e2a\u8bad\u7ec3\u7684\u4e0d\u540c\u9636\u6bb5\u8fdb\u884c\u8fd9\u4e9b\u8bc4\u4f30\uff0c\u4ee5\u6bd4\u8f83\u6bcf\u79cd\u8bed\u8a00\u7684\u5b66\u4e60\u8fc7\u7a0b\u3002\u8be5\u7814\u7a76\u7684\u6838\u5fc3\u53d1\u73b0\u662f\uff1a\u4e0e\u82f1\u8bed\u76f8\u6bd4\uff0c<mark data-type=\"tech_methods\" data-id=\"e8e4513e-695d-49e6-9155-31ae453de372\">GPT-2<\/mark> \u5f88\u96be\u5b66\u4e60\u300c\u4e0d\u53ef\u80fd\u8bed\u8a00\u300d\uff0c\u8fd9\u6311\u6218\u4e86\u4e54\u59c6\u65af\u57fa\u7b49\u4eba\u7684\u4e3b\u5f20\u3002<\/section>\n<section><\/section>\n<section>\u66f4\u91cd\u8981\u7684\u662f\uff0c\u8be5\u7814\u7a76\u5e0c\u671b\u5176\u65b9\u6cd5\u80fd\u591f\u5f00\u8f9f\u4e00\u6761\u5bcc\u6709\u6210\u6548\u7684\u63a2\u7a76\u8def\u7ebf\uff0c\u8ba9\u4e0d\u540c\u7684 LLM \u67b6\u6784\u5728\u5404\u79cd\u300c\u4e0d\u53ef\u80fd\u8bed\u8a00\u300d\u4e0a\u8fdb\u884c\u6d4b\u8bd5\uff0c\u4ee5\u4e86\u89e3\u5982\u4f55\u5c06 LLM \u7528\u4f5c\u8ba4\u77e5\u548c\u7c7b\u578b\u5b66\u8c03\u67e5\u5de5\u5177\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-88ba9778df1682aa1fcadb84bfabf8e6.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-88ba9778df1682aa1fcadb84bfabf8e6.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 2\uff1aWhy are Sensitive Functions Hard for Transformers?<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aMichael Hahn, Mark Rofin<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u8428\u5c14\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2402.09963<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u8bba\u6587\u7b80\u4ecb\uff1a\u5b9e\u9a8c\u7814\u7a76\u5df2\u7ecf\u786e\u5b9a\u4e86 transformer \u7684\u4e00\u7cfb\u5217\u53ef\u5b66\u4e60\u6027\u504f\u7f6e\u548c\u5c40\u9650\u6027\uff0c\u4f8b\u5982\u5b66\u4e60\u8ba1\u7b97 PARITY \u7b49\u7b80\u5355\u5f62\u5f0f\u8bed\u8a00\u7684\u6301\u7eed\u56f0\u96be\uff0c\u4ee5\u53ca\u5bf9\u4f4e\u5ea6\uff08low-degree\uff09\u51fd\u6570\u7684\u504f\u7f6e\u3002\u7136\u800c\uff0c\u7406\u8bba\u7406\u89e3\u4ecd\u7136\u6709\u9650\uff0c\u73b0\u6709\u7684\u8868\u8fbe\u7406\u8bba\u8981\u4e48\u9ad8\u4f30\u8981\u4e48\u4f4e\u4f30\u73b0\u5b9e\u7684\u5b66\u4e60\u80fd\u529b\u3002<\/section>\n<section><\/section>\n<section>\u8be5\u7814\u7a76\u8bc1\u660e\uff0c\u5728 transformer \u67b6\u6784\u4e0b\uff0c<mark data-type=\"concepts\" data-id=\"4c38563a-2d9b-439e-bfb4-21d209eeff3e\">\u635f\u5931\u51fd\u6570<\/mark>\u666f\u89c2\uff08loss landscape\uff09\u53d7\u5230\u8f93\u5165\u7a7a\u95f4\u7075\u654f\u5ea6\u7684\u9650\u5236\uff1a\u8f93\u51fa\u5bf9\u8f93\u5165\u4e32\u7684\u8bb8\u591a\u90e8\u5206\u654f\u611f\u7684 transformer \u4f4d\u4e8e<mark data-type=\"concepts\" data-id=\"2e982b73-88e2-41e8-a430-f7ae5a9af4bf\">\u53c2\u6570<\/mark>\u7a7a\u95f4\u4e2d\u7684\u5b64\u7acb\u70b9\uff0c\u5bfc\u81f4\u6cdb\u5316\u4e2d\u7684\u4f4e\u7075\u654f\u5ea6\u504f\u7f6e\u3002<\/section>\n<section><\/section>\n<section>\u8be5\u7814\u7a76\u4ece\u7406\u8bba\u4e0a\u548c\u5b9e\u9a8c\u4e0a\u8868\u660e\uff0c\u8be5\u7406\u8bba\u7edf\u4e00\u4e86\u5173\u4e8e transformer \u5b66\u4e60\u80fd\u529b\u548c\u504f\u7f6e\u7684\u5e7f\u6cdb\u5b9e\u9a8c\u89c2\u5bdf\uff0c\u4f8b\u5982\u5b83\u4eec\u5bf9\u4f4e\u7075\u654f\u5ea6\u548c\u4f4e\u5ea6\u7684\u6cdb\u5316\u504f\u7f6e\uff0c\u4ee5\u53ca\u5947\u5076\u6821\u9a8c\u957f\u5ea6\u6cdb\u5316\u7684\u56f0\u96be\u3002\u8fd9\u8868\u660e\uff0c\u4e86\u89e3 transformer \u7684\u5f52\u7eb3\u504f\u7f6e\uff08inductive biases\uff09\u4e0d\u4ec5\u9700\u8981\u7814\u7a76\u5176\u539f\u5219\u4e0a\u7684\u8868\u8fbe\u80fd\u529b\uff0c\u8fd8\u9700\u8981\u7814\u7a76\u5176<mark data-type=\"concepts\" data-id=\"4c38563a-2d9b-439e-bfb4-21d209eeff3e\">\u635f\u5931\u51fd\u6570<\/mark>\u666f\u89c2\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-78cf5ff9961e666dabb2f4c09f7c67b7.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-78cf5ff9961e666dabb2f4c09f7c67b7.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 3\uff1aDeciphering Oracle Bone Language with Diffusion Models<br \/><\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aHaisu Guan, Huanxin Yang, Xinyu Wang, Shengwei Han \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u534e\u4e2d\u79d1\u6280\u5927\u5b66\u3001\u963f\u5fb7\u83b1\u5fb7\u5927\u5b66\u3001\u5b89\u9633\u5e08\u8303\u5b66\u9662\u3001\u534e\u5357\u7406\u5de5\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2406.00684<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u8bba\u6587\u7b80\u4ecb\uff1a\u7532\u9aa8\u6587\uff08Oracle Bone Script\uff0cOBS\uff09\u8d77\u6e90\u4e8e\u7ea6 3000 \u5e74\u524d\u7684\u4e2d\u56fd\u5546\u671d\uff0c\u662f\u8bed\u8a00\u53f2\u4e0a\u7684\u57fa\u77f3\uff0c\u65e9\u4e8e\u8bb8\u591a\u65e2\u5b9a\u7684\u4e66\u5199\u7cfb\u7edf\u3002\u5c3d\u7ba1\u53d1\u73b0\u4e86\u6570\u5343\u4efd\u94ed\u6587\uff0c\u4f46\u4ecd\u6709\u5927\u91cf\u7684\u7532\u9aa8\u6587\u672a\u88ab\u7834\u8bd1\uff0c\u4ece\u800c\u4e3a\u8fd9\u4e00\u53e4\u8001\u7684\u8bed\u8a00\u8499\u4e0a\u4e86\u4e00\u5c42\u795e\u79d8\u7684\u9762\u7eb1\u3002\u73b0\u4ee3 AI \u6280\u672f\u7684\u51fa\u73b0\u4e3a\u7532\u9aa8\u6587\u7834\u8bd1\u5f00\u8f9f\u4e86\u65b0\u7684\u9886\u57df\uff0c\u5bf9\u4e25\u91cd\u4f9d\u8d56\u5927\u578b\u6587\u672c\u8bed\u6599\u5e93\u7684\u4f20\u7edf NLP \u65b9\u6cd5\u63d0\u51fa\u4e86\u6311\u6218\u3002<\/section>\n<section><\/section>\n<section>\u672c\u6587\u4ecb\u7ecd\u4e86\u4e00\u79cd\u91c7\u7528<mark data-type=\"tech_tasks\" data-id=\"ba1ea8a1-a705-4f89-8348-ec71bae840dc\">\u56fe\u50cf\u751f\u6210<\/mark>\u6280\u672f\u7684\u65b0\u65b9\u6cd5\uff0c\u5f00\u53d1\u51fa\u4e86\u9488\u5bf9\u7532\u9aa8\u6587\u7834\u8bd1\u4f18\u5316\u7684\u6269\u6563\u6a21\u578b Oracle Bone Script Decipher (OBSD)\u3002\u5229\u7528\u6761\u4ef6\u6269\u6563\u7b56\u7565\uff0cOBSD \u4e3a\u7532\u9aa8\u6587\u7834\u8bd1\u751f\u6210\u4e86\u91cd\u8981\u7684\u7ebf\u7d22\uff0c\u5e76\u4e3a \u53e4\u4ee3\u8bed\u8a00\u7684 AI \u8f85\u52a9\u5206\u6790\u5f00\u8f9f\u4e86\u65b0\u65b9\u5411\u3002\u4e3a\u4e86\u9a8c\u8bc1\u6709\u6548\u6027\uff0c\u7814\u7a76\u8005\u5728\u7532\u9aa8\u6587\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u4e86\u5e7f\u6cdb\u7684\u5b9e\u9a8c\uff0c\u5b9a\u91cf\u7ed3\u679c\u8bc1\u660e\u4e86 OBSD \u7684\u6709\u6548\u6027\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-16bbde29b7e9b1626ba4515c611c6427.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-16bbde29b7e9b1626ba4515c611c6427.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 4\uff1aCausal Estimation of Memorisation Profiles<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aPietro Lesci, Clara Meister, Thomas Hofmann, Andreas Vlachos, Tiago Pimentel<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u5251\u6865\u5927\u5b66\u3001\u82cf\u9ece\u4e16\u8054\u90a6\u7406\u5de5\u5b66\u9662<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2406.04327<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u8bba\u6587\u7b80\u4ecb\uff1a\u7406\u89e3<mark data-type=\"tech_tasks\" data-id=\"bf35ef94-d956-4033-a533-0c0828308c36\">\u8bed\u8a00\u6a21\u578b<\/mark>\u4e2d\u7684\u8bb0\u5fc6\u5177\u6709\u5b9e\u9645\u548c\u793e\u4f1a\u610f\u4e49\uff0c\u4f8b\u5982\u7814\u7a76\u6a21\u578b\u7684\u8bad\u7ec3\u52a8\u6001\u6216\u9632\u6b62\u7248\u6743\u4fb5\u6743\u3002\u4ee5\u5f80\u7684\u7814\u7a76\u5c06\u8bb0\u5fc6\u5b9a\u4e49\u4e3a\u300c\u4f7f\u7528\u5b9e\u4f8b\u8fdb\u884c\u7684\u8bad\u7ec3\u300d\u5bf9\u300c\u6a21\u578b\u9884\u6d4b\u8be5\u5b9e\u4f8b\u7684\u80fd\u529b\u300d\u7684\u56e0\u679c\u5173\u7cfb\u3002\u8fd9\u4e2a\u5b9a\u4e49\u4f9d\u8d56\u4e8e\u4e00\u4e2a\u53cd\u4e8b\u5b9e\uff1a\u89c2\u5bdf\u5982\u679c\u6a21\u578b\u6ca1\u6709\u770b\u5230\u8be5\u5b9e\u4f8b\u4f1a\u53d1\u751f\u4ec0\u4e48\u7684\u80fd\u529b\u3002\u73b0\u6709\u7684\u65b9\u6cd5\u96be\u4ee5\u63d0\u4f9b\u5bf9\u8fd9\u79cd\u53cd\u4e8b\u5b9e\u7684\u8ba1\u7b97\u6548\u7387\u548c\u51c6\u786e\u6027\u4f30\u8ba1\u3002\u6b64\u5916\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u901a\u5e38\u4f30\u8ba1\u6a21\u578b\u67b6\u6784\u7684\u8bb0\u5fc6\uff0c\u800c\u4e0d\u662f\u7279\u5b9a\u6a21\u578b\u5b9e\u4f8b\u7684\u8bb0\u5fc6\u3002<\/section>\n<section><\/section>\n<section>\u672c\u6587\u586b\u8865\u4e86\u4e00\u4e2a\u91cd\u8981\u7a7a\u767d\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u8ba1\u91cf\u7ecf\u6d4e\u5b66\u7684\u5dee\u5f02 &#8211; \u5dee\u5f02\u8bbe\u8ba1\u6765\u4f30\u8ba1\u8bb0\u5fc6\u7684\u5168\u65b0\u3001\u539f\u5219\u6027\u548c\u9ad8\u6548\u65b9\u6cd5\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u6cd5\uff0c\u7814\u7a76\u8005\u5728\u6574\u4e2a\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4ec5\u89c2\u5bdf\u6a21\u578b\u5728\u4e00\u5c0f\u90e8\u5206\u5b9e\u4f8b\u4e0a\u7684\u884c\u4e3a\u6765\u63cf\u8ff0\u6a21\u578b\u7684\u8bb0\u5fc6\u6982\u51b5\uff0c\u5373\u5176\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u8bb0\u5fc6\u8d8b\u52bf\u3002\u5728\u4f7f\u7528 Pythia \u6a21\u578b\u5957\u4ef6\u8fdb\u884c\u5b9e\u9a8c\u65f6\uff0c\u4ed6\u4eec\u53d1\u73b0\u8bb0\u5fc6 (i) \u5728\u8f83\u5927\u6a21\u578b\u4e2d\u66f4\u5f3a\u5927\u3001\u66f4\u6301\u4e45\uff0c(ii) \u7531\u6570\u636e\u987a\u5e8f\u548c<mark data-type=\"concepts\" data-id=\"444ad8d1-7262-46c9-94b7-fe0aa9227006\">\u5b66\u4e60\u7387<\/mark>\u51b3\u5b9a\uff0c\u4ee5\u53ca (iii) \u5728\u4e0d\u540c\u6a21\u578b\u5927\u5c0f\u4e4b\u95f4\u5177\u6709\u7a33\u5b9a\u7684\u8d8b\u52bf\uff0c\u56e0\u6b64\u8f83\u5927\u6a21\u578b\u4e2d\u7684\u8bb0\u5fc6\u53ef\u4ee5\u4ece\u8f83\u5c0f\u6a21\u578b\u4e2d\u9884\u6d4b\u51fa\u6765\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-f2edf7bb66722de8a028d81b642cc40e.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-f2edf7bb66722de8a028d81b642cc40e.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 5\uff1aAya Model: An Instruction Finetuned Open-Access Multilingual Language Model<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aAhmet \u00dcst\u00fcn, Viraat Aryabumi, Zheng Xin Yong, Wei-Yin Ko \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1aCohere\u3001\u5e03\u6717\u5927\u5b66\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2402.07827<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u8bba\u6587\u7b80\u4ecb\uff1a\u5927\u578b<mark data-type=\"tech_tasks\" data-id=\"bf35ef94-d956-4033-a533-0c0828308c36\">\u8bed\u8a00\u6a21\u578b<\/mark> (LLM) \u7684\u6700\u65b0\u7a81\u7834\u96c6\u4e2d\u5728\u5c11\u6570\u6570\u636e\u4e30\u5bcc\u7684\u8bed\u8a00\u4e0a\u3002\u5982\u4f55\u624d\u80fd\u5c06\u7a81\u7834\u7684\u9014\u5f84\u6269\u5c55\u5230\u5176\u4ed6\u8bed\u8a00\u4e4b\u5916\uff1f\u8be5\u7814\u7a76\u5f15\u5165\u4e86\u00a0Aya\uff0c\u8fd9\u662f\u4e00\u79cd\u5927\u89c4\u6a21\u591a\u8bed\u8a00\u751f\u6210<mark data-type=\"tech_tasks\" data-id=\"bf35ef94-d956-4033-a533-0c0828308c36\">\u8bed\u8a00\u6a21\u578b<\/mark>\uff0c\u5b83\u9075\u5faa 101 \u79cd\u8bed\u8a00\u6307\u4ee4\uff0c\u5176\u4e2d\u8d85\u8fc7 50% \u7684\u8bed\u8a00\u88ab\u89c6\u4e3a\u8d44\u6e90\u8f83\u5c11\u3002Aya \u5728\u5927\u591a\u6570\u4efb\u52a1\u4e0a\u7684\u8868\u73b0\u90fd\u4f18\u4e8e mT0 \u548c\u00a0BLOOMZ\uff0c\u540c\u65f6\u8986\u76d6\u7684\u8bed\u8a00\u6570\u91cf\u662f mT0 \u548c BLOOMZ \u7684\u4e24\u500d\u3002<\/section>\n<section><\/section>\n<section>\u6b64\u5916\uff0c\u8be5\u7814\u7a76\u8fd8\u5f15\u5165\u4e86\u5e7f\u6cdb\u7684\u65b0\u8bc4\u4f30\u5957\u4ef6\uff0c\u5c06\u591a\u8bed\u8a00\u8bc4\u4f30\u7684\u6700\u65b0\u6c34\u5e73\u6269\u5c55\u5230 99 \u79cd\u8bed\u8a00\u3002\u6700\u540e\uff0c\u8be5\u7814\u7a76\u5bf9\u6700\u4f73\u5fae\u8c03\u6df7\u5408\u7ec4\u6210\u3001\u6570\u636e<mark data-type=\"tech_tasks\" data-id=\"30ffd95b-36fa-4e6f-b863-926410835b10\">\u526a\u679d<\/mark>\u4ee5\u53ca\u6a21\u578b\u7684\u6bd2\u6027\u3001\u504f\u5dee\u548c\u5b89\u5168\u6027\u8fdb\u884c\u4e86\u8be6\u7ec6\u8c03\u67e5\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-5e90516259f11d12ab14eb834588a1d1.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-5e90516259f11d12ab14eb834588a1d1.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 6\uff1aSemisupervised Neural Proto-Language Reconstruction<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aLiang Lu \u3001 Peirong Xie \u3001 David R. Mortensen<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1aCMU\u3001\u5357\u52a0\u5dde\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2406.05930<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u8fd9\u9879\u5f00\u521b\u6027\u7684\u7814\u7a76\u65e8\u5728\u534a\u81ea\u52a8\u5316\u5386\u53f2<mark data-type=\"concepts\" data-id=\"e495445a-c189-4783-b0fb-17dcd3bfc9de\">\u8bed\u8a00\u5b66<\/mark>\u4e2d\u7684\u539f\u578b\u8bed\u8a00<mark data-type=\"tech_tasks\" data-id=\"213a4d47-596d-4465-9111-a933baa1ee9f\">\u91cd\u6784<\/mark>\u4efb\u52a1\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u534a\u76d1\u7763\u67b6\u6784\u3002\u901a\u8fc7\u5728\u300c\u6bcd\u8bed &#8211; \u539f\u578b\u300d<mark data-type=\"tech_tasks\" data-id=\"213a4d47-596d-4465-9111-a933baa1ee9f\">\u91cd\u6784<\/mark>\u4e2d\u5f15\u5165\u300c\u539f\u578b &#8211; \u6bcd\u8bed\u300d\u53cd\u5c04\u8fc7\u7a0b\uff0c\u8fd9\u79cd\u65b9\u6cd5\u4f18\u4e8e\u4e4b\u524d\u7684\u76d1\u7763\u65b9\u6cd5\u3002\u8fd9\u7bc7\u8bba\u6587\u5f88\u597d\u5730\u5c55\u793a\u4e86\u73b0\u4ee3\u8ba1\u7b97\u6a21\u578b\uff08\u5982\u795e\u7ecf\u7f16\u7801 &#8211; \u89e3\u7801\u5668\uff09\u5982\u4f55\u4e3a<mark data-type=\"concepts\" data-id=\"e495445a-c189-4783-b0fb-17dcd3bfc9de\">\u8bed\u8a00\u5b66<\/mark>\u4f5c\u51fa\u7684\u8d21\u732e\u3002\u00a0<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-6029a114d5f2149a50d2702586d958f6.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-6029a114d5f2149a50d2702586d958f6.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 7\uff1aNatural Language Satisfiability: Exploring the Problem Distribution and Evaluating Transformer-based Language Models\uff08\u672a\u516c\u5f00\uff09<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aTharindu Madusanka\u3001Ian Pratt-Hartmann\u3001Riza Batista-Navarro<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u8be5\u8bba\u6587\u6e05\u6670\u5730\u63cf\u8ff0\u4e86\u4e00\u4e2a\u7528\u4e8e<mark data-type=\"concepts\" data-id=\"95a97f4b-79d2-4bbc-91ae-300f074dff9f\">\u903b\u8f91<\/mark>\u63a8\u7406\u7684\u5408\u6210\u8bc4\u4f30\u6570\u636e\u96c6\u3002\u8fd9\u662f\u5bf9\u5927\u91cf\u63a8\u7406\u6570\u636e\u96c6\u7684\u4e00\u79cd\u826f\u597d\u8865\u5145\uff0c\u56e0\u4e3a\u8fd9\u4e9b\u6570\u636e\u96c6\u4e2d\u5e76\u4e0d\u660e\u786e\u6d4b\u91cf\u54ea\u4e9b\u80fd\u529b\u3002\u4ece\u7406\u8bba\u4e0a\u8bb2\uff0c\u786e\u5b9e\u6709\u7406\u7531\u9884\u671f\u67d0\u4e9b\u5b50\u96c6\u6bd4\u5176\u4ed6\u5b50\u96c6\u66f4\u96be\uff0c\u800c\u8fd9\u4e9b\u9884\u671f\u5728\u8bba\u6587\u4e2d\u5f97\u5230\u4e86\u9a8c\u8bc1\u3002\u5728\u6bcf\u4e2a\u7c7b\u522b\u4e2d\uff0c\u4f5c\u8005\u90fd\u7279\u522b\u6ce8\u610f\u62bd\u53d6\u90a3\u4e9b\u771f\u6b63\u5177\u6709\u6311\u6218\u6027\u7684\u6848\u4f8b\u3002\u00a0<\/section>\n<section><\/section>\n<section><strong>\u65f6\u95f4\u68c0\u9a8c\u5956<\/strong><\/section>\n<section><\/section>\n<section>ACL \u65f6\u95f4\u68c0\u9a8c\u5956\u5956\u52b1\u7684\u662f\u5bf9<mark data-type=\"tech_tasks\" data-id=\"c8ff5114-6cbb-49ca-8a89-3ee2826be0b4\">\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/mark>\u548c\u8ba1\u7b97<mark data-type=\"concepts\" data-id=\"e495445a-c189-4783-b0fb-17dcd3bfc9de\">\u8bed\u8a00\u5b66<\/mark>\u9886\u57df\u4ea7\u751f\u957f\u671f\u5f71\u54cd\u7684\u8363\u8a89\u8bba\u6587\uff0c\u5206\u4e3a 10 \u5e74\u524d\uff082014 \u5e74\uff09\u548c 25 \u5e74\u524d\uff081999 \u5e74\uff09\u4e24\u4e2a\u5956\u9879\uff0c\u6bcf\u5e74\u6700\u591a\u9881\u53d1\u4e24\u7bc7\u8bba\u6587\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-09f848909b34b79d72f8135a5a2b3470.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-09f848909b34b79d72f8135a5a2b3470.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 1\uff1a<mark data-type=\"tech_methods\" data-id=\"20ed7263-df85-4418-8838-ad1b39c5efcd\">GloVe<\/mark>: Global Vectors for Word Representation<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aJeffrey Pennington, Richard Socher, Christopher D. Manning<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u65af\u5766\u798f\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/aclanthology.org\/D14-1162.pdf<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u8bba\u6587\u7b80\u4ecb\uff1a\u5b66\u4e60\u8bcd\u7684\u5411\u91cf\u7a7a\u95f4\u8868\u5f81\u7684\u65b9\u6cd5\u5df2\u7ecf\u5728\u4f7f\u7528\u5411\u91cf<mark data-type=\"tech_tasks\" data-id=\"c1ae9de3-3159-4e1a-8bff-4ec534f247a7\">\u7b97\u672f<\/mark>\u6355\u83b7\u7ec6\u7c92\u5ea6\u7684\u8bed\u4e49\u548c\u53e5\u6cd5\u89c4\u5219\u65b9\u9762\u53d6\u5f97\u4e86\u6210\u529f\uff0c\u4f46\u662f\u53e5\u6cd5\u89c4\u5219\u4ecd\u4e0d\u900f\u660e\u3002\u8be5\u7814\u7a76\u5206\u6790\u5e76\u660e\u786e\u4e86\u4e3a\u4e86\u8ba9\u53e5\u6cd5\u89c4\u5219\u51fa\u73b0\u5728\u8bcd\u5411\u91cf\u4e2d\uff0c\u6a21\u578b\u9700\u8981\u5177\u5907\u54ea\u4e9b\u5c5e\u6027\u3002<\/section>\n<section><\/section>\n<section>\u8be5\u7814\u7a76\u63d0\u51fa\u4e86\u4e00\u4e2a\u65b0\u7684\u5168\u5c40\u5bf9\u6570\u7ebf\u6027\u56de\u5f52\u6a21\u578b \u2014\u2014<mark data-type=\"tech_methods\" data-id=\"20ed7263-df85-4418-8838-ad1b39c5efcd\">GloVe<\/mark>\uff0c\u65e8\u5728\u5b66\u4e60\u8bcd\u7684\u5411\u91cf\u8868\u5f81\u3002\u8be5\u6a21\u578b\u7ed3\u5408\u4e86\u5168\u5c40<mark data-type=\"concepts\" data-id=\"775b8e6a-d0e4-42cd-a837-d587e4181470\">\u77e9\u9635\u5206\u89e3<\/mark>\u548c\u5c40\u90e8\u4e0a\u4e0b\u6587\u7a97\u53e3\u4e24\u79cd\u65b9\u6cd5\u7684\u4f18\u70b9\u3002<\/section>\n<section><\/section>\n<section><mark data-type=\"tech_methods\" data-id=\"20ed7263-df85-4418-8838-ad1b39c5efcd\">GloVe<\/mark> \u5728\u8bcd\u7c7b\u6bd4\u4efb\u52a1\u4e0a\u53d6\u5f97\u4e86 75% \u7684\u6700\u4f73\u6027\u80fd\uff0c\u5e76\u5728\u8bcd\u76f8\u4f3c\u6027\u4efb\u52a1\u548c<mark data-type=\"tech_tasks\" data-id=\"e97cdb6b-e5e2-473d-9545-e8c6d52a5010\">\u547d\u540d\u5b9e\u4f53\u8bc6<\/mark>\u522b\u65b9\u9762\u4f18\u4e8e\u76f8\u5173\u6a21\u578b\u3002<\/section>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a<mark data-type=\"tech_tasks\" data-id=\"2feeb7b3-2bea-4238-9c79-0d235ffc71cc\">\u8bcd\u5d4c\u5165<\/mark>\u662f 2013 \u5e74\u81f3 2018 \u5e74\u95f4<mark data-type=\"tech_tasks\" data-id=\"c8ff5114-6cbb-49ca-8a89-3ee2826be0b4\">\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/mark>\uff08NLP\uff09<mark data-type=\"tech_methods\" data-id=\"01946acc-d031-4c0e-909c-f062643b7273\">\u6df1\u5ea6\u5b66\u4e60<\/mark>\u65b9\u6cd5\u7684\u57fa\u77f3\uff0c\u5e76\u4e14\u6301\u7eed\u53d1\u6325\u7740\u663e\u8457\u5f71\u54cd\u3002\u5b83\u4eec\u4e0d\u4ec5\u589e\u5f3a\u4e86 NLP \u4efb\u52a1\u7684\u6027\u80fd\uff0c\u800c\u4e14\u5728\u8ba1\u7b97<mark data-type=\"concepts\" data-id=\"f0c7ff48-80c0-4a6a-828d-be410fc65c99\">\u8bed\u4e49\u5b66<\/mark>\u65b9\u9762\u4e5f\u4ea7\u751f\u4e86\u663e\u8457\u5f71\u54cd\uff0c\u4f8b\u5982\u5728\u8bcd\u8bed\u76f8\u4f3c\u6027\u548c\u7c7b\u6bd4\u4e0a\u3002\u4e24\u79cd\u6700\u6709\u5f71\u54cd\u529b\u7684<mark data-type=\"tech_tasks\" data-id=\"2feeb7b3-2bea-4238-9c79-0d235ffc71cc\">\u8bcd\u5d4c\u5165<\/mark>\u65b9\u6cd5\u53ef\u80fd\u662f skip-gram\/CBOW \u548c <mark data-type=\"tech_methods\" data-id=\"20ed7263-df85-4418-8838-ad1b39c5efcd\">GloVe<\/mark>\u3002\u4e0e skip-gram \u76f8\u6bd4\uff0c<mark data-type=\"tech_methods\" data-id=\"20ed7263-df85-4418-8838-ad1b39c5efcd\">GloVe<\/mark> \u63d0\u51fa\u5f97\u8f83\u665a\u3002\u5b83\u7684\u76f8\u5bf9\u4f18\u52bf\u5728\u4e8e\u6982\u5ff5\u4e0a\u7684\u7b80\u5355\u6027\uff0c\u76f4\u63a5\u6839\u636e\u8bcd\u4e4b\u95f4\u7684\u5206\u5e03\u7279\u6027\u4f18\u5316\u5411\u91cf\u7a7a\u95f4\u76f8\u4f3c\u6027\uff0c\u800c\u4e0d\u662f\u4ece\u7b80\u5316\u7684\u8bed\u8a00\u5efa\u6a21\u89d2\u5ea6\u95f4\u63a5\u4f5c\u4e3a\u4e00\u7ec4<mark data-type=\"concepts\" data-id=\"2e982b73-88e2-41e8-a430-f7ae5a9af4bf\">\u53c2\u6570<\/mark>\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-52d529730b00285579c26c70b1236a3f.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-52d529730b00285579c26c70b1236a3f.png\"><\/a><\/p>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-88c8f5ba25903d0e193605207722a64a.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-88c8f5ba25903d0e193605207722a64a.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587 2\uff1aMeasures of Distributional Similarity<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1a<mark data-type=\"experts\" data-id=\"78e9994b-2154-45ed-8621-787ef5493016\">Lillian Lee<\/mark><\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u5eb7\u5948\u5c14\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/aclanthology.org\/P99-1004.pdf<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u8bba\u6587\u7b80\u4ecb\uff1a\u4f5c\u8005\u7814\u7a76\u4e86\u5206\u5e03\u76f8\u4f3c\u6027\u5ea6\u91cf\uff0c\u76ee\u7684\u662f\u63d0\u9ad8\u5bf9\u672a\u89c1\u5171\u73b0\u4e8b\u4ef6\u7684\u6982\u7387\u4f30\u8ba1\u3002\u4ed6\u4eec\u7684\u8d21\u732e\u6709\u4e09\u4e2a\u65b9\u9762\uff1a\u5bf9\u4e00\u7cfb\u5217\u5e7f\u6cdb\u7684\u5ea6\u91cf\u65b9\u6cd5\u8fdb\u884c\u5b9e\u8bc1\u6bd4\u8f83\uff1b\u57fa\u4e8e\u5b83\u4eec\u6240\u5305\u542b\u7684\u4fe1\u606f\u5bf9\u76f8\u4f3c\u6027\u51fd\u6570\u8fdb\u884c\u5206\u7c7b\uff1b\u5f15\u5165\u4e86\u4e00\u79cd\u65b0\u7684\u51fd\u6570\uff0c\u8be5\u51fd\u6570\u5728\u8bc4\u4f30\u6f5c\u5728\u4ee3\u7406\u5206\u5e03\u65b9\u9762\u66f4\u4e3a\u4f18\u8d8a\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-004b34d4110f4c56512e80b0435e84c8.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-004b34d4110f4c56512e80b0435e84c8.png\"><\/a><\/p>\n<section><strong>\u7ec8\u8eab\u6210\u5c31\u5956<\/strong><\/section>\n<section><\/section>\n<section>ACL \u7684\u7ec8\u8eab\u6210\u5c31\u5956\u9881\u7ed9\u4e86 Ralph Grishman\u3002Ralph Grishman \u662f\u7ebd\u7ea6\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u7cfb\u7684\u6559\u6388\uff0c\u4e13\u6ce8\u4e8e<mark data-type=\"tech_tasks\" data-id=\"c8ff5114-6cbb-49ca-8a89-3ee2826be0b4\">\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/mark>\uff08NLP\uff09\u9886\u57df\u7684\u7814\u7a76\u3002\u4ed6\u662f Proteus Project \u7684\u521b\u59cb\u4eba\uff0c\u8be5\u9879\u76ee\u5728<mark data-type=\"tech_tasks\" data-id=\"ec0b0595-c797-4111-99b3-b533b4fca9c1\">\u4fe1\u606f\u62bd\u53d6<\/mark>\uff08IE\uff09\u65b9\u9762\u505a\u51fa\u4e86\u91cd\u5927\u8d21\u732e\uff0c\u63a8\u52a8\u4e86\u8be5\u9886\u57df\u7684\u53d1\u5c55\u3002<\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-950b0ae19c9230031b7c423dd4b387b0.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-950b0ae19c9230031b7c423dd4b387b0.png\"><\/a><\/p>\n<section>\u4ed6\u8fd8\u5f00\u53d1\u4e86 Java Extraction Toolkit (JET)\uff0c\u8fd9\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684<mark data-type=\"tech_tasks\" data-id=\"ec0b0595-c797-4111-99b3-b533b4fca9c1\">\u4fe1\u606f\u62bd\u53d6<\/mark>\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u8bed\u8a00\u5206\u6790\u7ec4\u4ef6\uff0c\u5982\u53e5\u5b50\u5206\u5272\u3001\u547d\u540d\u5b9e\u4f53\u6807\u6ce8\u3001\u65f6\u95f4\u8868\u8fbe\u6807\u6ce8\u4e0e<mark data-type=\"concepts\" data-id=\"a815978e-ab05-479e-b6b4-30176b3d5427\">\u89c4\u8303\u5316<\/mark>\u3001<mark data-type=\"tech_tasks\" data-id=\"5b71072d-4494-43eb-8730-302c4a90f45e\">\u8bcd\u6027\u6807\u6ce8<\/mark>\u3001\u90e8\u5206\u89e3\u6790\u548c\u5171\u6307\u5206\u6790\u3002\u8fd9\u4e9b\u7ec4\u4ef6\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u5e94\u7528\u7ec4\u5408\u6210\u7ba1\u9053\uff0c\u65e2\u53ef\u7528\u4e8e\u5355\u4e2a\u53e5\u5b50\u7684\u4ea4\u4e92\u5206\u6790\uff0c\u4e5f\u53ef\u7528\u4e8e\u6574\u7bc7\u6587\u6863\u7684\u6279\u91cf\u5206\u6790\u3002\u6b64\u5916\uff0cJET \u8fd8\u63d0\u4f9b\u4e86\u7b80\u5355\u5de5\u5177\u7528\u4e8e\u6587\u6863\u7684\u6807\u6ce8\u548c\u663e\u793a\uff0c\u5e76\u5305\u62ec\u5b8c\u6574\u7684\u6d41\u7a0b\u4ee5\u6309\u7167 ACE\uff08\u81ea\u52a8\u5185\u5bb9\u62bd\u53d6\uff09\u89c4\u8303\u8fdb\u884c\u5b9e\u4f53\u3001\u5173\u7cfb\u548c\u4e8b\u4ef6\u7684\u62bd\u53d6\u3002\u00a0<\/section>\n<section><\/section>\n<section>Grishman \u6559\u6388\u7684\u5de5\u4f5c\u6db5\u76d6\u4e86\u591a\u4e2a NLP \u7684\u6838\u5fc3\u95ee\u9898\uff0c\u5e76\u5bf9\u73b0\u4ee3\u8bed\u8a00\u5904\u7406\u6280\u672f\u4ea7\u751f\u4e86\u6df1\u8fdc\u7684\u5f71\u54cd\u3002\u00a0<\/section>\n<section><\/section>\n<section><strong>35 \u7bc7\u6770\u51fa\u8bba\u6587<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 1\uff1aQuantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aZhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1aCMU\u3001<mark data-type=\"institutions\" data-id=\"4349d6b9-40ff-4e23-a237-f9ace91c6c81\">\u6e05\u534e\u5927\u5b66<\/mark>\u3001\u9e4f\u57ce\u5b9e\u9a8c\u5ba4\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2401.07159<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 2\uff1aL-Eval: Instituting Standardized Evaluation for Long Context Language Models<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aChenxin An, Shansan Gong, Ming Zhong, Xingjian Zhao, Mukai Li, Jun Zhang, Lingpeng Kong, Xipeng Qiu<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a<mark data-type=\"institutions\" data-id=\"9eafd97f-5be8-4cf5-885d-e44b0047c26f\">\u590d\u65e6\u5927\u5b66<\/mark>\u3001\u9999\u6e2f\u5927\u5b66\u3001\u4f0a\u5229\u8bfa\u4f0a\u5927\u5b66\u5384\u5df4\u7eb3 &#8211; \u9999\u69df\u5206\u6821\u3001\u4e0a\u6d77 AI Lab<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2307.11088<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 3\uff1aCausal-Guided Active Learning for Debiasing Large Language Models<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/openreview.net\/forum?id=idp_1Q6F-lC<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 4\uff1aCausalGym: Benchmarking causal interpretability methods on linguistic tasks<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aAryaman Arora, Dan Jurafsky, Christopher Potts<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u65af\u5766\u798f\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2402.12560<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 5\uff1aDon&#8217;t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aShangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, Yulia Tsvetkov<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u534e\u76db\u987f\u5927\u5b66\u3001\u52a0\u5dde\u5927\u5b66\u4f2f\u514b\u5229\u5206\u6821\u3001\u9999\u6e2f\u79d1\u6280\u5927\u5b66\u3001CMU<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2402.00367<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 6\uff1aSpeech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aMarco Gaido, Sara Papi, Matteo Negri, Luisa Bentivogli<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u610f\u5927\u5229\u5e03\u9c81\u8bfa\u30fb\u51ef\u65af\u52d2\u57fa\u91d1\u4f1a<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2402.12025<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 7\uff1aMust NLP be Extractive?<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aSteven Bird<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u67e5\u5c14\u65af\u8fbe\u5c14\u6587\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/drive.google.com\/file\/d\/1hvF7_WQrou6CWZydhymYFTYHnd3ZIljV\/view<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 8\uff1aIRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aIndraneil Paul\u3001Goran Glava\u0161\u3001Iryna Gurevych<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u8fbe\u59c6\u65bd\u5854\u7279\u5de5\u4e1a\u5927\u5b66\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2403.03894<\/section>\n<section><\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587 9\uff1aMultiLegalPile: A 689GB Multilingual Legal Corpus<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aMatthias St\u00fcrmer \u3001 Veton Matoshi \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u4f2f\u5c14\u5c3c\u5927\u5b66\u3001\u65af\u5766\u798f\u5927\u5b66\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2306.02069<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 10\uff1aPsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1a \u00a0Zaibin Zhang \u3001 Yongting Zhang \u3001 Lijun Li \u3001 Hongzhi Gao \u3001 Lijun Wang \u3001 Huchuan Lu \u3001 Feng Zhao \u3001 Yu Qiao\u3001Jing Shao<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u4e0a\u6d77<mark data-type=\"concepts\" data-id=\"2d28aa9c-942d-471d-bd96-8bfefb7144e0\">\u4eba\u5de5\u667a\u80fd<\/mark>\u5b9e\u9a8c\u5ba4\u3001\u5927\u8fde\u7406\u5de5\u5927\u5b66\u3001\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2401.11880<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 11\uff1aCan Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aDongjin Kang\u3001Sunghwan Kim \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u5ef6\u4e16\u5927\u5b66\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2402.13211<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 12\uff1aPolitical Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aPaul R\u00f6ttger \u3001 Valentin Hofmann \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u535a\u79d1\u5c3c\u5927\u5b66\u3001\u827e\u4f26<mark data-type=\"concepts\" data-id=\"2d28aa9c-942d-471d-bd96-8bfefb7144e0\">\u4eba\u5de5\u667a\u80fd<\/mark>\u7814\u7a76\u9662\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2402.16786<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 13\uff1aSame Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aMosh Levy \u3001 Alon Jacoby \u3001 Yoav Goldberg<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u5df4\u4f0a\u5170\u5927\u5b66\u3001\u827e\u4f26<mark data-type=\"concepts\" data-id=\"2d28aa9c-942d-471d-bd96-8bfefb7144e0\">\u4eba\u5de5\u667a\u80fd<\/mark>\u7814\u7a76\u9662<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2402.14848<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 14\uff1aDo Llamas Work in English? On the Latent Language of Multilingual Transformers<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aChris Wendler \u3001 Veniamin Veselovsky \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u6d1b\u6851\u8054\u90a6\u7406\u5de5\u5b66\u9662<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2402.10588<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 15\uff1aGetting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aZachary Horvitz \u3001 Jingru Chen \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u54e5\u4f26\u6bd4\u4e9a\u5927\u5b66\u3001\u6d1b\u6851\u8054\u90a6\u7406\u5de5\u5b66\u9662<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2403.00794<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 16\uff1aEstimating the Level of Dialectness Predicts Inter-annotator Agreement in Multi-dialect Arabic Datasets<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aAmr Keleg, Walid Magdy, Sharon Goldwater<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u7231\u4e01\u5821\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2405.11282<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 17\uff1aG-DlG: Towards Gradient-based Dlverse and hiGh-quality Instruction Data Selection for Machine Translation<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aXingyuan Pan, Luyang Huang, Liyan Kang, Zhicheng Liu, Yu Lu, Shanbo Cheng<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1aByteDance Research<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2405.12915<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 18\uff1aMedia Framing: A typology and Survey of Computational Approaches Across Disciplines<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aYulia Otmakhova, Shima Khanehzar, Lea Frermann<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/openreview.net\/pdf?id=9AV_zM56pwj<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 19\uff1aSPZ: A Semantic Perturbation-based Data Augmentation Method with Zonal-Mixing for Alzheimer&#8217;s Disease Detection<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aFangFang Li\u3001Cheng Huang\u3001PuZhen Su\u3001Jie Yin<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 20\uff1aGreed is All You Need: An Evaluation of Tokenizer Inference Methods<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u5185\u76d6\u592b\u672c\u30fb\u53e4\u91cc\u5b89\u5927\u5b66\u3001\u9ebb\u7701\u7406\u5de5\u5b66\u9662<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aOmri Uzan\u3001Craig W.Schmidt\u3001Chris Tanner\u3001Yuval Pinter<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2403.01289<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 21\uff1aLanguage Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn&#8217;t<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u5723\u6bcd\u5927\u5b66\uff08\u7f8e\u56fd\uff09<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aChihiro Taquchi\u3001David Chiang<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2406.09202<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 22\uff1aSteering Llama 2 via Contrastive Activation Addition<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1aAnthropic\u3001\u54c8\u4f5b\u5927\u5b66\u3001\u54e5\u5ef7\u6839\u5927\u5b66\uff08\u5fb7\u56fd\uff09\u3001 Center for Human-Compatible AI<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aNina Rimsky\u3001Nick Gabrieli\u3001<mark data-type=\"concepts\" data-id=\"2194d698-2e0a-4e6c-8f36-92d01507185d\">Julia<\/mark>n Schulz\u3001Meg Tong\u3001Evan J Hubinger\u3001Alexander Matt Turner<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2312.06681<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 23\uff1aEconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a<mark data-type=\"institutions\" data-id=\"4349d6b9-40ff-4e23-a237-f9ace91c6c81\">\u6e05\u534e\u5927\u5b66<\/mark> &#8211; \u6df1\u5733\u56fd\u9645\u7814\u7a76\u751f\u9662\u3001<mark data-type=\"institutions\" data-id=\"4349d6b9-40ff-4e23-a237-f9ace91c6c81\">\u6e05\u534e\u5927\u5b66<\/mark><\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aNian Li\u3001Chen Gao\u3001Mingyu Li\u3001Yong Li\u3001Qingmin Liao<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2310.10436<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 24\uff1aM4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u9999\u6e2f\u4e2d\u6587\u5927\u5b66\u3001<mark data-type=\"institutions\" data-id=\"48d62db6-3e47-4e93-8b9d-b1283a7ea419\">\u534e\u4e3a<\/mark>\u8bfa\u4e9a\u65b9\u821f\u5b9e\u9a8c\u5ba4\u3001\u9999\u6e2f\u79d1\u6280\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aWai-Chung Kwan\u3001Xingshan Zeng\u3001Yufei Wang\u3001Yusen Sun\u3001Liangyou Li\u3001Lifeng Shang\u3001Qun Liu\u3001Kam-Fai Wong<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2310.19240<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 25\uff1aCHECKWHY: Causal Fact Verification via Argument Structure<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aJiasheng Si\u3001Yibo Zhao\u3001Yingjie Zhu\u3001Haiyang Zhu\u3001Wenpeng Lu\u3001Deyu Zhou<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 26\uff1aOn Efficient and Statistical Quality Estimation for Data Annotation<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aJan-Christoph Klie\uff0cJuan Haladjian\uff0cMarc Kirchner\uff0cRahul Nair<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1aUKP Lab,\u3001TU Darmstadt \u3001\u82f9\u679c\u516c\u53f8<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2405.11919<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 27\uff1aEmulated Disalignment: Safety Alignment for Large Language Models May Backfire!<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aZhanhui Zhou, Jie Liu, Zhichen Dong, Jiaheng Liu, Chao Yang, Wanli Ouyang, Yu Qiao<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u4e0a\u6d77<mark data-type=\"concepts\" data-id=\"2d28aa9c-942d-471d-bd96-8bfefb7144e0\">\u4eba\u5de5\u667a\u80fd<\/mark>\u5b9e\u9a8c\u5ba4<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2402.12343<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 28\uff1aIndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aMohammed Safi Ur Rahman Khan, Priyam Mehta, Ananth Sankar \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1aNilekani Centre at AI4Bharat\u3001\u5370\u5ea6\u7406\u5de5\u5b66\u9662\uff08\u9a6c\u5fb7\u62c9\u65af\uff09\u3001\u5fae\u8f6f\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2403.06350<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 29\uff1aMultiPICo: Multilingual Perspectivist lrony Corpus<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aSilvia Casola, Simona Frenda, Soda Marem Lo, Erhan Sezerer\u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u90fd\u7075\u5927\u5b66\u3001aequa-tech\u3001\u4e9a\u9a6c\u900a\u5f00\u53d1\u4e2d\u5fc3\uff08\u610f\u5927\u5229\uff09\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/assets.amazon.science\/08\/83\/9b686f424c89b08e8fa0a6e1d020\/multipico-multilingual-perspectivist-irony-corpus.pdf<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 30\uff1aMMToM-QA: Multimodal Theory of Mind Question Answering<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aChuanyang Jin, Yutong Wu, Jing Cao, jiannan Xiang\u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u7ebd\u7ea6\u5927\u5b66\u3001\u54c8\u4f5b\u5927\u5b66\u3001MIT\u3001\u52a0\u5dde\u5927\u5b66\u5723\u8fed\u6208\u5206\u6821\u3001\u5f17\u5409\u5c3c\u4e9a\u5927\u5b66\u3001\u7ea6\u7ff0\u970d\u666e\u91d1\u65af\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2401.08743<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 31\uff1aMAP&#8217;s not dead yet: Uncovering true language model modes by conditioning away degeneracy<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aDavis Yoshida, Kartik Goyal, Kevin Gimpel<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u4e30\u7530\u5de5\u4e1a\u5927\u5b66\u829d\u52a0\u54e5\u5206\u6821\u3001\u4f50\u6cbb\u4e9a\u7406\u5de5\u5b66\u9662<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2311.08817<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 32\uff1aNounAtlas: Filling the Gap in Nominal Semantic Role Labeling<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aRoberto Navigli, Marco Lo Pinto, Pasquale Silvestri\u7b49<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 33\uff1aThe Earth is Flat because.. lnvestigating LLMs&#8217; Belief towards Misinformation via PersuasiveConversation<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aRongwu Xu, Brian S. Lin, Shujian Yang, Tiangi Zhang\u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a<mark data-type=\"institutions\" data-id=\"4349d6b9-40ff-4e23-a237-f9ace91c6c81\">\u6e05\u534e\u5927\u5b66<\/mark>\u3001\u4e0a\u6d77\u4ea4\u901a\u5927\u5b66\u3001\u65af\u5766\u798f\u5927\u5b66\u3001\u5357\u6d0b\u7406\u5de5\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2312.09085<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 34\uff1aLet&#8217;s Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aSe Jin Park, Chae Won Kim, Hyeongseop Rha, Minsu Kim\u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u97e9\u56fd\u79d1\u5b66\u6280\u672f\u9662\uff08KAIST\uff09<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2406.07867<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<ul>\n<li>\n<section>\u8bba\u6587 35\uff1aWord Embeddings Are Steers for Language Models<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aChi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek F. Abdelzaher, Heng Ji<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u4f0a\u5229\u8bfa\u4f0a\u5927\u5b66\u5384\u5df4\u7eb3 &#8211; \u9999\u69df\u5206\u6821<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2305.12798<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section><strong>\u6700\u4f73\u4e3b\u9898\u8bba\u6587\u5956<\/strong><\/section>\n<p><a href=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-7f5712ae38b2ae4d11fd465669de6899.png\" data-fancybox=\"images\" data-fancybox=\"gallery\"><img decoding=\"async\" src=\"https:\/\/17aitech.com\/wp-content\/uploads\/2024\/08\/frc-7f5712ae38b2ae4d11fd465669de6899.png\"><\/a><\/p>\n<section><strong>\u8bba\u6587\uff1aOLMo\uff1aAccelerating the Science of Language Models<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aDirk Groeneveld \u3001 Iz Beltagy \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u827e\u4f26<mark data-type=\"concepts\" data-id=\"2d28aa9c-942d-471d-bd96-8bfefb7144e0\">\u4eba\u5de5\u667a\u80fd<\/mark>\u7814\u7a76\u9662\u3001\u534e\u76db\u987f\u5927\u5b66\u7b49<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2402.00838<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u8fd9\u9879\u5de5\u4f5c\u662f\u671d\u7740\u5927\u578b<mark data-type=\"tech_tasks\" data-id=\"bf35ef94-d956-4033-a533-0c0828308c36\">\u8bed\u8a00\u6a21\u578b<\/mark>\u8bad\u7ec3\u7684\u900f\u660e\u6027\u548c\u53ef\u91cd\u590d\u6027\u8fc8\u51fa\u7684\u91cd\u8981\u4e00\u6b65\uff0c\u8fd9\u662f\u793e\u533a\u5728\u53d6\u5f97\u8fdb\u5c55\uff08\u6216\u81f3\u5c11\u4e3a\u4e86\u8ba9\u975e\u884c\u4e1a\u5de8\u5934\u7684\u5176\u4ed6\u7814\u7a76\u8005\u4e5f\u80fd\u8d21\u732e\u8fdb\u5c55\uff09\u65b9\u9762\u6025\u9700\u7684\u3002\u00a0<\/section>\n<section><\/section>\n<section><strong>\u8d44\u6e90\u8bba\u6587\u5956<\/strong><\/section>\n<section><\/section>\n<section>3 \u7bc7\u8bba\u6587\u83b7\u5f97 Resource Paper Award\u3002<\/section>\n<section><\/section>\n<section><strong>\u8bba\u6587 1\uff1aLatxa: An Open Language Model and Evaluation Suite for Basque<\/strong><\/section>\n<section><strong>\u673a\u6784\uff1a\u897f\u73ed\u7259\u5df4\u65af\u514b\u5927\u5b66<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aJulen Etxaniz\u3001Oscar Sainz\u3001Naiara Perez\u3001Itziar Aldabe\u3001German Rigau\u3001Eneko Agirre\u3001Aitor Ormazabal\u3001Mikel Artetxe\u3001Aitor Soroa<\/section>\n<\/li>\n<li>\n<section>\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2403.20266<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u8be5\u8bba\u6587\u7ec6\u81f4\u63cf\u8ff0\u4e86\u8bed\u6599\u6536\u96c6\u3001\u6570\u636e\u96c6\u8bc4\u4f30\u7684\u7ec6\u8282\u3002\u5c3d\u7ba1\u662f\u5df4\u65af\u514b\u8bed\u8a00\u76f8\u5173\u7814\u7a76\uff0c\u8fd9\u4e00\u65b9\u6cd5\u8bba\u53ef\u6269\u5c55\u5230\u5176\u4ed6\u4f4e\u8d44\u6e90\u8bed\u8a00\u5927\u6a21\u578b\u7684\u6784\u5efa\u4e0a\u3002<\/section>\n<section><\/section>\n<section><strong>\u8bba\u6587 2\uff1aDolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u673a\u6784\uff1a\u827e\u4f26<mark data-type=\"concepts\" data-id=\"2d28aa9c-942d-471d-bd96-8bfefb7144e0\">\u4eba\u5de5\u667a\u80fd<\/mark>\u7814\u7a76\u9662\u3001\u52a0\u5dde\u4f2f\u514b\u5229\u5927\u5b66\u7b49<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aLuca Soldaini\u3001Rodney Kinney \u7b49<\/section>\n<\/li>\n<li>\n<section>\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2402.00159<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u8be5\u8bba\u6587\u5c55\u793a\u4e86\u8bad\u7ec3\u5927<mark data-type=\"tech_tasks\" data-id=\"bf35ef94-d956-4033-a533-0c0828308c36\">\u8bed\u8a00\u6a21\u578b<\/mark>\u51c6\u5907\u6570\u636e\u96c6\u65f6<mark data-type=\"tech_tasks\" data-id=\"6c2b1832-a1ec-4823-952d-1f7c0f3890ec\">\u6570\u636e\u7ba1\u7406<\/mark>\u7684\u91cd\u8981\u6027\u3002\u8fd9\u4e3a\u793e\u533a\u5185\u5e7f\u5927\u4eba\u7fa4\u63d0\u4f9b\u4e86\u975e\u5e38\u6709\u4ef7\u503c\u7684\u6d1e\u89c1\u3002<\/section>\n<section><\/section>\n<section><strong>\u8bba\u6587 3\uff1aAppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u673a\u6784\uff1a\u7ebd\u7ea6\u5dde\u7acb\u5927\u5b66\u77f3\u6eaa\u5206\u6821\u3001\u827e\u4f26<mark data-type=\"concepts\" data-id=\"2d28aa9c-942d-471d-bd96-8bfefb7144e0\">\u4eba\u5de5\u667a\u80fd<\/mark>\u7814\u7a76\u9662\u7b49<\/section>\n<\/li>\n<li>\n<section>\u4f5c\u8005\uff1aHarsh Trivedi, Tushar Khot \u7b49<\/section>\n<\/li>\n<li>\n<section>\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2407.18901<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u8be5\u7814\u7a76\u662f\u6784\u5efa\u4ea4\u4e92\u73af\u5883\u6a21\u62df\u4e0e\u8bc4\u4f30\u65b9\u9762\u975e\u5e38\u91cd\u8981\u3001\u60ca\u8273\u7684\u5de5\u4f5c\u3002\u5b83\u5c06\u9f13\u52b1\u5927\u5bb6\u4e3a\u793e\u533a\u591a\u591a\u4ea7\u51fa\u786c\u6838\u52a8\u6001<mark data-type=\"concepts\" data-id=\"308c3a45-0fee-4ec6-858e-85b15f440fc0\">\u57fa\u51c6<\/mark>\u3002<\/section>\n<section><\/section>\n<section><strong>\u793e\u4f1a\u5f71\u54cd\u529b\u5956<\/strong><\/section>\n<section><\/section>\n<section>3 \u7bc7\u8bba\u6587\u83b7\u5f97 Social Impact Award\u3002<\/section>\n<section><\/section>\n<section><strong>\u8bba\u6587 1\uff1aHow Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aYi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang\u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u5f17\u5409\u5c3c\u4e9a\u7406\u5de5\u5927\u5b66\u3001\u4e2d\u56fd\u4eba\u6c11\u5927\u5b66\u3001\u52a0\u5dde\u5927\u5b66\u6234\u7ef4\u65af\u5206\u6821\u3001\u65af\u5766\u798f\u5927\u5b66<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2401.06373<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u672c\u6587\u63a2\u8ba8\u4e86 AI \u5b89\u5168\u4e3b\u9898 \u2014\u2014 \u8d8a\u72f1\uff0c\u7814\u7a76\u4e86\u793e\u4f1a\u79d1\u5b66\u7814\u7a76\u9886\u57df\u5185\u5f00\u53d1\u7684\u4e00\u79cd\u65b9\u6cd5\u3002\u8be5\u7814\u7a76\u975e\u5e38\u6709\u8da3\uff0c\u5e76\u6709\u53ef\u80fd\u5bf9\u793e\u533a\u4ea7\u751f\u91cd\u5927\u5f71\u54cd\u3002<\/section>\n<section><\/section>\n<section><strong>\u8bba\u6587 2\uff1aDIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aFahim Faisal, Orevaoghene Ahia, Aarohi Srivastava, Kabir Ahuja \u7b49<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u4e54\u6cbb\u6885\u68ee\u5927\u5b66\u3001\u534e\u76db\u987f\u5927\u5b66\u3001\u5723\u6bcd\u5927\u5b66\u3001 RC Athena<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2403.11009<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u65b9\u8a00\u53d8\u5f02\u662f NLP \u548c<mark data-type=\"concepts\" data-id=\"2d28aa9c-942d-471d-bd96-8bfefb7144e0\">\u4eba\u5de5\u667a\u80fd<\/mark>\u9886\u57df\u672a\u80fd\u5f97\u5230\u5145\u5206\u7814\u7a76\u7684\u73b0\u8c61\u3002\u7136\u800c\uff0c\u4ece\u8bed\u8a00\u548c\u793e\u4f1a\u7684\u89d2\u5ea6\u6765\u770b\uff0c\u5b83\u7684\u7814\u7a76\u5177\u6709\u6781\u9ad8\u7684\u4ef7\u503c\uff0c\u5bf9\u5e94\u7528\u4e5f\u6709\u91cd\u8981\u7684\u5f71\u54cd\u3002\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u4e2a\u975e\u5e38\u65b0\u9896\u7684<mark data-type=\"concepts\" data-id=\"308c3a45-0fee-4ec6-858e-85b15f440fc0\">\u57fa\u51c6<\/mark>\u6765\u7814\u7a76 LLM \u65f6\u4ee3\u7684\u8fd9\u4e2a\u95ee\u9898\u3002<\/section>\n<section><\/section>\n<section><strong>\u8bba\u6587 3\uff1aHaving Beer after Prayer? Measuring Cultural Bias in Large LanguageModels<\/strong><\/section>\n<section><\/section>\n<ul>\n<li>\n<section>\u4f5c\u8005\uff1aTarek Naous, Michael J. Ryan, Alan Ritter, Wei Xu<\/section>\n<\/li>\n<li>\n<section>\u673a\u6784\uff1a\u4f50\u6cbb\u4e9a\u7406\u5de5\u5b66\u9662<\/section>\n<\/li>\n<li>\n<section>\u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2305.14456<\/section>\n<\/li>\n<\/ul>\n<section><\/section>\n<section>\u83b7\u5956\u7406\u7531\uff1a\u672c\u6587\u5c55\u793a\u4e86 LLM \u65f6\u4ee3\u7684\u4e00\u4e2a\u91cd\u8981\u95ee\u9898\uff1a\u6587\u5316\u504f\u89c1\u3002\u672c\u6587\u7814\u7a76\u4e86\u963f\u62c9\u4f2f\u6587\u5316\u548c\u8bed\u8a00\u73af\u5883\uff0c\u7ed3\u679c\u8868\u660e\uff0c\u5728\u8bbe\u8ba1 LLM \u65f6\uff0c\u6211\u4eec\u9700\u8981\u8003\u8651\u6587\u5316\u5dee\u5f02\u3002\u56e0\u6b64\uff0c\u540c\u6837\u7684\u7814\u7a76\u53ef\u4ee5\u590d\u5236\u5230\u5176\u4ed6\u6587\u5316\u4e2d\uff0c\u4ee5\u6982\u62ec\u548c\u8bc4\u4f30\u5176\u4ed6\u6587\u5316\u662f\u5426\u4e5f\u53d7\u5230\u8fd9\u4e2a\u95ee\u9898\u7684\u5f71\u54cd\u3002<\/section>\n<p>\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:<a href=\"https:\/\/www.jiqizhixin.com\/articles\/2024-08-15-4\" target=\"_blank\">ACL 2024\u5956\u9879\u516c\u5e03\uff1a\u534e\u79d1\u5927\u7834\u8bd1\u7532\u9aa8\u6587\u6700\u4f73\u8bba\u6587\u4e4b\u4e00\u3001GloVe\u65f6\u95f4\u68c0\u9a8c\u5956<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:ACL 2024\u5956\u9879\u516c [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[27],"tags":[70,68,65,67],"class_list":["post-35120","post","type-post","status-publish","format-standard","hentry","category-news","tag-agent","tag-68","tag-65","tag-67"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - 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