\u51e0\u5929\u524d\uff0cICLR 2024 \u7684\u6700\u7ec8\u63a5\u6536\u7ed3\u679c\u51fa\u6765\u4e86\u3002<\/p>\n
\u5927\u5bb6\u5e94\u8be5\u8fd8\u8bb0\u5f97\uff0cMamba \u88ab ICLR 2024 \u5927\u4f1a Decision Pending\uff08\u5f85\u5b9a\uff09\u7684\u6d88\u606f\u5728 1 \u6708\u4efd\u5f15\u53d1\u8fc7\u4e00\u6ce2\u793e\u533a\u70ed\u8bae\u3002<\/p>\n
\u5f53\u65f6\uff0c\u591a\u4f4d\u9886\u57df\u5185\u7684\u7814\u7a76\u8005\u5206\u6790\uff0cDecision Pending \u7684\u610f\u601d\u662f\u5ef6\u8fdf\u51b3\u5b9a\uff0c\u867d\u7136\u4e5f\u53ef\u80fd\u4f1a\u88ab\u62d2\uff0c\u4f46\u8fd9\u7bc7\u8bba\u6587\u5f97\u5230\u4e86 8\/8\/6\/3 \u7684\u6253\u5206\uff0c\u6309\u7406\u8bf4\u4e0d\u81f3\u4e8e\u771f\u88ab\u62d2\u3002<\/p>\n
\u5982\u4eca\uff0cDecision \u5df2\u51fa\uff0cMamba \u5f7b\u5e95\u88ab\u62d2\uff0c\u60ac\u7740\u7684\u5fc3\u7ec8\u4e8e\u6b7b\u4e86\u3002<\/p>\n
\u300cMamba\u300d\u53d1\u5e03\u4e4b\u521d\u5373\u88ab\u89c6\u4e3a\u300cTransformer \u7684\u5f3a\u52b2\u7ade\u4e89\u8005\u300d\uff0c\u5b83\u662f\u4e00\u79cd\u9009\u62e9\u6027\u72b6\u6001\u7a7a\u95f4\u6a21\u578b\uff08selective state space model\uff09\uff0c\u5728\u8bed\u8a00\u5efa\u6a21\u65b9\u9762\u53ef\u4ee5\u5ab2\u7f8e\u751a\u81f3\u51fb\u8d25 Transformer\u3002\u800c\u4e14\uff0c\u5b83\u53ef\u4ee5\u968f\u4e0a\u4e0b\u6587\u957f\u5ea6\u7684\u589e\u52a0\u5b9e\u73b0\u7ebf\u6027\u6269\u5c55\uff0c\u5176\u6027\u80fd\u5728\u5b9e\u9645\u6570\u636e\u4e2d\u53ef\u63d0\u9ad8\u5230\u767e\u4e07 token \u957f\u5ea6\u5e8f\u5217\uff0c\u5e76\u5b9e\u73b0 5 \u500d\u7684\u63a8\u7406\u541e\u5410\u91cf\u63d0\u5347\u3002<\/p>\n
\u4f46\u5bf9\u4e8e ICLR \u5ba1\u7a3f\u4eba\u6765\u8bf4\uff0c\u8fd9\u7bc7\u8bba\u6587\u8fd8\u5b58\u5728\u91cd\u5927\u7f3a\u9677\uff08\u81f3\u5c11\u9488\u5bf9\u5f53\u524d\u7248\u672c\uff09\u3002<\/p>\n
\u91cd\u65b0\u67e5\u770b OpenReview \u9875\u9762\u4e4b\u540e\uff0c\u6211\u4eec\u53d1\u73b0\u4e86\u65b0\u7684\u5ba1\u7a3f\u610f\u89c1\u3002<\/p>\n
ICLR \u533a\u57df\u4e3b\u5e2d\u7ed9\u51fa\u7684\u6700\u7ec8\u8bf4\u6cd5\u662f\uff1a\u8bba\u6587\u4f7f\u7528\u7684\u8bc4\u4f30\u65b9\u6cd5\u6709\u4e89\u8bae\u3002<\/p>\n
\u5ba1\u7a3f\u610f\u89c1\u6574\u7406\u5982\u4e0b\uff1a<\/p>\n
\u672c\u6587\u4ecb\u7ecd\u4e86\u4e00\u79cd\u4e3a\u8fdc\u8ddd\u79bb\u8bed\u8a00\u5efa\u6a21\u800c\u8bbe\u8ba1\u7684\u65b0\u578b\u72b6\u6001\u7a7a\u95f4\u6a21\u578b\u53d8\u4f53\u3002\u5b9e\u9a8c\u8868\u660e\uff0c\u5728\u8bed\u8a00\u5efa\u6a21\u4efb\u52a1\u7684\u56f0\u60d1\u5ea6\u6307\u6807\u4e0b\uff0c\u8be5\u6a21\u578b\u4e0e\u73b0\u6709\u6a21\u578b\u76f8\u6bd4\u6709\u663e\u8457\u8fdb\u6b65\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u4e24\u4f4d\u5ba1\u7a3f\u4eba\u7ed9\u51fa\u4e86\u975e\u5e38\u79ef\u6781\u7684\u8bc4\u4ef7\uff08\u5c3d\u7ba1\u5176\u4e2d\u4e00\u4f4d\u5ba1\u7a3f\u4eba\u5728\u8bed\u8a00\u6a21\u578b\u65b9\u9762\u7ecf\u9a8c\u6709\u9650\uff09\u3002\u7136\u800c\uff0c\u7b2c\u4e09\u4f4d\u5ba1\u7a3f\u4eba\uff0c\u4e00\u4f4d\u5728\u8bed\u8a00\u6a21\u578b\u65b9\u9762\u66f4\u6709\u7ecf\u9a8c\u7684\u4e13\u5bb6\uff0c\u63d0\u51fa\u4e86\u4e24\u4e2a\u4e0e\u57fa\u51c6\u548c\u8bc4\u4f30\u6307\u6807\u6709\u5173\u7684\u91cd\u5927\u95ee\u9898\uff1a<\/p>\n
1. \u7f3a\u5c11 LRA\uff08Long Range Arena\uff09\u7684\u7ed3\u679c\uff1a\u5ba1\u7a3f\u4eba\u5f3a\u8c03\u7f3a\u5c11 LRA \u7684\u7ed3\u679c\uff0c\u800c LRA \u662f\u516c\u8ba4\u7684\u957f\u5e8f\u5217\u5efa\u6a21\u57fa\u51c6\u3002\u5728\u4e4b\u524d\u7684\u72b6\u6001\u7a7a\u95f4\u6a21\u578b\u7814\u7a76\u4e2d\uff0cLRA \u5df2\u6210\u4e3a\u60ef\u4f8b\uff0c\u56e0\u6b64\u5fc5\u987b\u5bf9\u5176\u8fdb\u884c\u5168\u9762\u8bc4\u4f30\u3002<\/p>\n
2. \u4f7f\u7528\u56f0\u60d1\u5ea6\u8fdb\u884c\u8bc4\u4f30\uff1a\u5ba1\u7a3f\u4eba\u8d28\u7591\u5c06\u56f0\u60d1\u5ea6\u4f5c\u4e3a\u4e3b\u8981\u8bc4\u4ef7\u6307\u6807\u7684\u505a\u6cd5\u3002\u8bba\u6587\u5f15\u7528\u4e86 Sun et al. (2021)\uff08\u300aDo Long-Range Language Models Actually Use Long-Range Context?\u300b\uff09\u7684\u89c2\u70b9\uff0c\u4ed6\u4eec\u8ba4\u4e3a\u8f83\u4f4e\u7684\u56f0\u60d1\u5ea6\u5e76\u4e0d\u4e00\u5b9a\u610f\u5473\u7740\u6700\u7ec8 NLP \u5e94\u7528\u7684\u5efa\u6a21\u80fd\u529b\u6709\u6240\u63d0\u9ad8\u3002Zhang et al. (2023)\uff08\u300aEfficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer\u300b\uff09\u8fdb\u4e00\u6b65\u52a0\u5f3a\u4e86\u4ed6\u4eec\u7684\u89c2\u70b9\uff0c\u4ed6\u4eec\u5f3a\u8c03\u4e86\u4e00\u4e9b transformer \u6a21\u578b\u7684\u5c40\u9650\u6027\uff0c\u8fd9\u4e9b\u6a21\u578b\u867d\u7136\u5b9e\u73b0\u4e86\u8f83\u4f4e\u7684\u56f0\u60d1\u5ea6\uff0c\u4f46\u5728\u751f\u6210\u4efb\u52a1\uff08\u5982\u6458\u8981\u548c\u95ee\u9898\u89e3\u7b54\uff09\u4e2d\u5374\u4e3e\u6b65\u7ef4\u8270\u3002<\/p>\n
\u6b64\u5916\uff0c\u8fd8\u6709\u4eba\u5bf9\u957f\u5e8f\u5217\u8bed\u8a00\u6a21\u578b\u5728\u77ed\u6587\u672c\u5e8f\u5217\u4e2d\u7684\u6f5c\u5728\u6027\u80fd\u5dee\u8ddd\u8868\u793a\u62c5\u5fe7\u3002\u6211\u5efa\u8bae\u52a0\u5165\u8865\u5145\u5b9e\u9a8c\u7ed3\u679c\u6765\u89e3\u51b3\u8fd9\u65b9\u9762\u7684\u95ee\u9898\u3002<\/p>\n
\u4e3a\u4e86\u8c03\u548c\u8fd9\u4e9b\u4e0d\u540c\u7684\u89c2\u70b9\uff0c\u6211\u4eec\u4e0e\u5ba1\u7a3f\u4eba du8a \u8fdb\u884c\u4e86\u8ba8\u8bba\uff0c\u968f\u540e\u53c8\u4e0e\u9ad8\u7ea7\u533a\u57df\u4e3b\u5e2d\u8fdb\u884c\u4e86\u8ba8\u8bba\u3002\u5728\u5bf9\u8bba\u6587\u8fdb\u884c\u7ec6\u81f4\u5ba1\u67e5\u5e76\u8003\u8651\u5230\u6240\u63d0\u51fa\u7684\u5408\u7406\u5173\u5207\u540e\uff0c\u6700\u7ec8\u51b3\u5b9a\u5efa\u8bae\u62d2\u7edd\u8be5\u8bba\u6587\u3002\u8fd9\u4e9b\u95ee\u9898\uff0c\u5c24\u5176\u662f\u4e0e\u5b9e\u9a8c\u65b9\u6cd5\u548c\u6240\u9009\u8bc4\u4ef7\u6307\u6807\u6709\u5173\u7684\u95ee\u9898\uff0c\u88ab\u8ba4\u4e3a\u662f\u5b9e\u8d28\u6027\u7684\uff0c\u5728\u6240\u63d0\u4f9b\u7684 rebuttal \u4e2d\u6ca1\u6709\u5f97\u5230\u5145\u5206\u89e3\u51b3\u3002\u6211\u4eec\u8ba4\u4e3a\uff0c\u901a\u8fc7\u589e\u52a0\u989d\u5916\u7684\u5b9e\u9a8c\u6765\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0c\u5bf9\u8bba\u6587\u5c06\u5927\u6709\u88e8\u76ca\u3002<\/p>\n
Mamba \u7684\u7ecf\u5386\uff0c\u8ba9\u4eba\u4eec\u60f3\u8d77\u4e86\u5341\u5e74\u524d\u7684\u4e00\u7bc7\u8bba\u6587\u3002<\/p>\n
\u56fe\u4e2d\u63d0\u5230\u7684\u662f\u5173\u4e8e\u7684 Word2vec \u9996\u7bc7\u8bba\u6587\u300aEfficient Estimation of Word Representations in Vector Space\u300b\uff0c\u7531 Tomas Mikolov \u7b49\u56db\u4f4d\u8c37\u6b4c\u7814\u7a76\u8005\u5171\u540c\u5b8c\u6210\u3002<\/p>\n
\u8fd9\u7bc7\u8bba\u6587\u5728 2013 \u5e74\u9996\u5c4a ICLR \u4f1a\u8bae\u88ab\u62d2\u4e86\uff0c\u5c3d\u7ba1\u5f53\u5e74\u7684\u63a5\u6536\u7387\u6bd4\u8f83\u9ad8\u3002\u53bb\u5e74\uff0c Tomas Mikolov \u5728\u68b3\u7406 Word2vec \u53d1\u5c55\u5386\u7a0b\u7684\u65f6\u5019\u8fd8\u9057\u61be\u63d0\u5230\uff1a\u300c\u8fd9\u8ba9\u6211\u60f3\u5230\u5ba1\u7a3f\u4eba\u9884\u6d4b\u8bba\u6587\u7684\u672a\u6765\u5f71\u54cd\u662f\u591a\u4e48\u56f0\u96be\u3002\u300d<\/p>\n
\u4f46\u7ec6\u770b\u4e4b\u4e0b\uff0cWord2vec \u88ab\u62d2\u7684\u539f\u56e0\u5012\u662f\u548c\u4e00\u822c\u8bba\u6587\u4e0d\u540c\u3002<\/p>\n
\u5728 OpenReview \u7684\u9875\u9762\uff0c\u6211\u4eec\u770b\u5230\u5f53\u65f6\u51e0\u4f4d\u5ba1\u7a3f\u4eba\u9488\u5bf9\u63d0\u4ea4\u7248\u672c\u7ed9\u5230\u4e86\u4e00\u6ce2\u610f\u89c1\uff0c\u6bd4\u5982\u8865\u5145\u5b9a\u4e49\u6a21\u578b\u7684\u65b9\u7a0b\u7b49\u7b49\u3002<\/p>\n
\u800c Tomas Mikolov \u7684\u56de\u590d\u6001\u5ea6\u504f\u5f3a\u786c\uff0c\u663e\u7136\u4e5f\u6ca1\u6709\u5145\u5206\u5b8c\u5584\u5bf9\u5e94\u6bcf\u6761\u5ba1\u7a3f\u610f\u89c1\u7684\u6750\u6599\uff0c\u5bfc\u81f4\u51e0\u4f4d\u5ba1\u7a3f\u4eba\u770b\u5b8c\u4e86 rebuttal\uff0c\u66f4\u751f\u6c14\u4e86\u3002<\/p>\n
\u4e00\u4f4d\u5ba1\u7a3f\u4eba\u6700\u7ec8\u7ed9\u51fa\u300cStrong Reject\u300d\uff1a<\/p>\n
\u53e6\u4e00\u4f4d\u5ba1\u7a3f\u4eba\u66fe\u7ed9\u51fa\u300c\u5927\u90e8\u5206\u5185\u5bb9\u6e05\u6670\u826f\u597d\u300d\u7684\u8bc4\u8bba\uff0c\u4f46\u540e\u6765\u4e5f\u4fee\u6539\u4e3a\u300cWeak Reject\u300d\uff1a<\/p>\n
\u8fd8\u6709\u4e00\u4f4d\u5ba1\u7a3f\u4eba\u76f4\u767d\u5730\u6307\u51fa\uff1a<\/p>\n
\u300c\u4ee4\u4eba\u9057\u61be\u7684\u662f\uff0c\u7b54\u8fa9\u4f5c\u8005\u4f3c\u4e4e\u53ea\u5173\u5fc3\u4ed6\u7684\u6a21\u578b\u548c\u6a21\u578b\u7ec4\u5408\u7684\u6bcf\u4e00\u4e2a\u53ef\u80fd\u7684\u8c03\u6574\uff0c\u5374\u5bf9\u5408\u7406\u7684\u79d1\u5b66\u5bf9\u6bd4\u8868\u73b0\u51fa\u5f3a\u70c8\u7684\u6f20\u89c6\u3002\u300d<\/p>\n
\u300c\u4f5c\u8005\u5199\u9053\uff0c\u6709\u8bb8\u591a\u663e\u800c\u6613\u89c1\u7684\u5b9e\u9645\u4efb\u52a1\uff0c\u4ed6\u4eec\u7684\u8bcd\u5411\u91cf\u5e94\u8be5\u6709\u6240\u5e2e\u52a9\uff0c\u4f46\u5374\u6ca1\u6709\u5c55\u793a\u6216\u63d0\u53ca\u4efb\u4f55\u4efb\u52a1\u3002\u300d<\/p>\n
\u300c\u9664\u4e86\u4ed6\u81ea\u5df1\u7684\u6a21\u578b\u3001\u6570\u636e\u96c6\u548c\u4efb\u52a1\u4e4b\u5916\uff0c\u4f5c\u8005\u4f3c\u4e4e\u66f4\u613f\u610f\u5ffd\u7565\u6240\u6709\u5176\u4ed6\u7684\u4e1c\u897f\u3002\u6211\u4ecd\u7136\u4e0d\u6e05\u695a\u662f\u6a21\u578b\u7684\u54ea\u4e2a\u90e8\u5206\u5e26\u6765\u4e86\u6027\u80fd\u63d0\u5347\u3002\u662f\u9876\u5c42\u4efb\u52a1\u8fd8\u662f\u8bcd\u5411\u91cf\u7684\u5e73\u5747\u5316\uff1f\u300d<\/p>\n
\u300c\u94fe\u63a5\u5230\u4f5c\u8005\u5728\u7ef4\u57fa\u767e\u79d1\u4e0a\u53d1\u8868\u7684\u4e00\u7bc7\u6587\u7ae0\u5e76\u4e0d\u80fd\u4f5c\u4e3a\u6709\u529b\u7684\u8bba\u636e\uff0c\u8fd8\u4e0d\u5982\u663e\u793a\u51fa\u6307\u51fa\u5b9e\u9645\u5dee\u5f02\u7684\u65b9\u7a0b\u5f0f\u3002\u7ecf\u8fc7\u5ba1\u7a3f\u4eba\u4e4b\u95f4\u7684\u8ba8\u8bba\uff0c\u6211\u4eec\u4e00\u81f4\u8ba4\u4e3a\u8bba\u6587\u7684\u4fee\u8ba2\u7248\u548c\u968f\u9644\u7684 rebuttal \u5e76\u6ca1\u6709\u89e3\u51b3\u5ba1\u7a3f\u4eba\u63d0\u51fa\u7684\u8bb8\u591a\u95ee\u9898\uff0c\u5ba1\u7a3f\u4eba\u7684\u8bb8\u591a\u95ee\u9898\uff08\u5982\u54ea\u4e9b\u6a21\u578b\u5305\u542b\u975e\u7ebf\u6027\uff09\u4ecd\u672a\u5f97\u5230\u56de\u7b54\u3002\u300d<\/p>\n
\u603b\u4e4b\uff0c\u8fd9\u6b21\u5ba1\u7a3f\u95f9\u5f97\u4e0d\u592a\u6109\u5feb\u3002<\/p>\n
\u540e\u6765\uff0c\u56db\u4f4d\u4f5c\u8005 Tomas Mikolov\u3001Kai Chen\u3001Greg Corrado\u3001Jeffrey Dean \u548c\u5f53\u65f6\u5728\u8c37\u6b4c\u4efb\u804c\u7684 Ilya Sutskever \u53c8\u5199\u4e86\u4e00\u7bc7\u5173\u4e8e Word2vec \u7684\u8bba\u6587\u300aDistributed Representations of Words and Phrases and their Compositionality\u300b\uff0c\u8f6c\u6295 NeurIPS \u4e14\u88ab\u987a\u5229\u63a5\u6536\u4e86\u3002<\/p>\n
\u53bb\u5e74\uff0c\u8fd9\u7bc7\u8bba\u6587\u8fd8\u83b7\u5f97\u4e86 NeurIPS 2023 \u7684\u65f6\u95f4\u68c0\u9a8c\u5956\uff0c\u83b7\u5956\u7406\u7531\u662f\u300c\u8fd9\u9879\u5de5\u4f5c\u5f15\u5165\u4e86\u5f00\u521b\u6027\u7684\u8bcd\u5d4c\u5165\u6280\u672f word2vec\uff0c\u5c55\u793a\u4e86\u4ece\u5927\u91cf\u975e\u7ed3\u6784\u5316\u6587\u672c\u4e2d\u5b66\u4e60\u7684\u80fd\u529b\uff0c\u63a8\u52a8\u4e86\u81ea\u7136\u8bed\u8a00\u5904\u7406\u65b0\u65f6\u4ee3\u7684\u5230\u6765\u300d\u3002<\/p>\n
\u53ef\u60dc\u7684\u662f\uff0c\u540e\u7eed\u51e0\u4f4d\u4f5c\u8005\u7684\u5173\u7cfb\u9677\u5165\u50f5\u5c40\uff0cTomas Mikolov \u900f\u9732\u7684\u7248\u672c\u662f\uff1a<\/p>\n
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