{"id":1075908,"date":"2024-04-18T10:34:00","date_gmt":"2024-04-18T17:34:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/?post_type=msr-blog-post&#038;p=1075908"},"modified":"2024-09-25T04:17:27","modified_gmt":"2024-09-25T11:17:27","slug":"new-arrival-in-research-10","status":"publish","type":"msr-blog-post","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/articles\/new-arrival-in-research-10\/","title":{"rendered":"\u4f4eGPU\u5229\u7528\u7387\u7684\u5b9e\u8bc1\u7814\u7a76\uff1b\u53ef\u89e3\u51b3\u6570\u5b66\u95ee\u9898\u7684\u6570\u636e\u5408\u6210\u65b0\u8303\u5f0f\uff1b\u5927\u89c4\u6a21\u5408\u6210\u6570\u5b66\u63a8\u7406\u7684\u6307\u4ee4\u5fae\u8c03\u6570\u636e\uff1b\u5927\u6a21\u578b\u6539\u8fdb\u63a8\u8350\u7cfb\u7edf"},"content":{"rendered":"\n<p>\u7f16\u8005\u6309\uff1a\u6b22\u8fce\u9605\u8bfb\u201c\u79d1\u7814\u4e0a\u65b0\u201d\u680f\u76ee\uff01\u201c\u79d1\u7814\u4e0a\u65b0\u201d\u6c47\u805a\u4e86\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662\u6700\u65b0\u7684\u521b\u65b0\u6210\u679c\u4e0e\u79d1\u7814\u52a8\u6001\u3002\u5728\u8fd9\u91cc\uff0c\u4f60\u53ef\u4ee5\u5feb\u901f\u6d4f\u89c8\u7814\u7a76\u9662\u7684\u4eae\u70b9\u8d44\u8baf\uff0c\u4fdd\u6301\u5bf9\u524d\u6cbf\u9886\u57df\u7684\u654f\u9510\u55c5\u89c9\uff0c\u540c\u65f6\u4e5f\u80fd\u627e\u5230\u5148\u8fdb\u5b9e\u7528\u7684\u5f00\u6e90\u5de5\u5177\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"\u6df1\u5ea6\u5b66\u4e60\u4f5c\u4e1a\u4f4egpu\u5229\u7528\u7387\u95ee\u9898\u7684\u5b9e\u8bc1\u7814\u7a76-icse-2024\">\u6df1\u5ea6\u5b66\u4e60\u4f5c\u4e1a\u4f4eGPU\u5229\u7528\u7387\u95ee\u9898\u7684\u5b9e\u8bc1\u7814\u7a76\uff08ICSE 2024\uff09<\/h3>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"372\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-1-768x372-1.png\" alt=\"An Empirical Study on Low GPU Utilization of Deep Learning Jobs\" class=\"wp-image-1075917\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-1-768x372-1.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-1-768x372-1-300x145.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-1-768x372-1-240x116.png 240w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<p>\u8bba\u6587\u94fe\u63a5\uff1a<a href=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/an-empirical-study-on-low-gpu-utilization-of-deep-learning-jobs\/\">https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/an-empirical-study-on-low-gpu-utilization-of-deep-learning-jobs\/<\/a><\/p>\n\n\n\n<p>\u8fd1\u5e74\u6765\uff0c\u6df1\u5ea6\u5b66\u4e60\u5728\u8bf8\u591a\u9886\u57df\u53d6\u5f97\u4e86\u663e\u8457\u6210\u5c31\uff0c\u5e76\u5728\u5404\u79cd\u667a\u80fd\u8f6f\u4ef6\u5e94\u7528\u4e2d\u626e\u6f14\u91cd\u8981\u89d2\u8272\u3002\u4e3a\u4e86\u66f4\u597d\u5730\u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u548c\u6d4b\u8bd5\uff0cIT \u4f01\u4e1a\u6784\u5efa\u4e86\u6df1\u5ea6\u5b66\u4e60\u5e73\u53f0\u5e76\u5728\u5e73\u53f0\u4e0a\u914d\u5907\u4e86\u5927\u91cf\u7684 GPU\u3002\u5728\u5fae\u8f6f\u516c\u53f8\u5185\u90e8\uff0c\u6bcf\u5929\u90fd\u6709\u6570\u767e\u540d\u5f00\u53d1\u8005\u5728\u6df1\u5ea6\u5b66\u4e60\u751f\u4ea7\u5e73\u53f0&#8211;Platform-X \u4e0a\u6267\u884c\u8bad\u7ec3\u548c\u6d4b\u8bd5\u4f5c\u4e1a\u3002GPU \u7684\u5229\u7528\u7387\u662f\u8861\u91cf\u4f5c\u4e1a\u8fd0\u884c\u65f6\u6027\u80fd\u548c\u6548\u7387\u7684\u5173\u952e\u6307\u6807\u3002\u4f4e GPU \u5229\u7528\u7387\u4f5c\u4e1a\u4e0d\u4ec5\u4f1a\u5bfc\u81f4\u8d44\u6e90\u6d6a\u8d39\uff0c\u8fd8\u4f1a\u663e\u8457\u964d\u4f4e\u751f\u4ea7\u529b\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"503\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-2-1024x503-1.png\" alt=\"An Empirical Study on Low GPU Utilization of Deep Learning Jobs | diagram\" class=\"wp-image-1075920\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-2-1024x503-1.png 1024w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-2-1024x503-1-300x147.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-2-1024x503-1-768x377.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-2-1024x503-1-240x118.png 240w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">\u56fe1\uff1aPlatform-X \u7684\u5de5\u4f5c\u6d41<\/figcaption><\/figure>\n\n\n\n<p>\u56fe1\u7b80\u8981\u8bf4\u660e\u4e86 Platform-X \u7684\u5de5\u4f5c\u6d41\u7a0b\u3002\u7528\u6237\u6307\u5b9a\u4f5c\u4e1a\u7684\u8d44\u6e90\u914d\u989d\u3001\u955c\u50cf\u3001\u542f\u52a8\u811a\u672c\u7b49\u914d\u7f6e\u3002\u8c03\u5ea6\u7a0b\u5e8f\u4f1a\u4f7f\u7528\u7fa4\u8c03\u5ea6\u7b97\u6cd5\u8c03\u5ea6\u4f5c\u4e1a\u548c\u5206\u914d\u8d44\u6e90\uff0c\u5e76\u5728\u4e00\u4e2a\u6216\u591a\u4e2a GPU \u8ba1\u7b97\u8282\u70b9\u4e0a\u5b9e\u4f8b\u5316\u5bb9\u5668\u3002\u5178\u578b\u7684\u6df1\u5ea6\u5b66\u4e60\u4f5c\u4e1a\u7684\u751f\u547d\u5468\u671f\u901a\u5e38\u5206\u4e3a\u4ee5\u4e0b\u56db\u4e2a\u9636\u6bb5\uff1a\u521d\u59cb\u5316\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u6a21\u578b\u9a8c\u8bc1\u3002<\/p>\n\n\n\n<p>\u6df1\u5ea6\u5b66\u4e60\u5df2\u6210\u4e3a\u73b0\u4ee3\u8f6f\u4ef6\u5e94\u7528\u7a0b\u5e8f\u7684\u57fa\u672c\u7ec4\u6210\u90e8\u5206\uff0c\u4e14\u4e91\u5e73\u53f0\u9010\u6e10\u4f5c\u4e3a\u8bad\u7ec3\u548c\u90e8\u7f72\u6a21\u578b\u4e3b\u8981\u57fa\u7840\u8bbe\u65bd\u3002\u4e86\u89e3\u6df1\u5ea6\u5b66\u4e60\u4f5c\u4e1a\u4e2d\u4f4e GPU \u5229\u7528\u7387\u7684\u539f\u56e0\u5e76\u5bfb\u6c42\u89e3\u51b3\u65b9\u6848\u81f3\u5173\u91cd\u8981\uff0c\u5176\u6709\u52a9\u4e8e\u63ed\u793a\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u4e2d\u72ec\u7279\u7684\u8f6f\u4ef6\u5de5\u7a0b\u6311\u6218\uff0c\u5e76\u6307\u5bfc\u5f00\u53d1\u9ad8\u8d28\u91cf\u3001\u4f4e\u6210\u672c\u7684\u8f6f\u4ef6\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n\n\n\n<p>\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662\u7684\u7814\u7a76\u5458\u4eec\u548c\u5fae\u8f6f Azure \u4e91\u5e73\u53f0\u90e8\u95e8\u7684\u5de5\u7a0b\u5e08\u4eec\u5408\u4f5c\uff0c\u5bf9\u5fae\u8f6f\u5185\u90e8\u6df1\u5ea6\u5b66\u4e60\u751f\u4ea7\u5e73\u53f0 Platform-X \u4e0a\u4f5c\u4e1a\u4f4e\u5229\u7528\u7387\u95ee\u9898\u8fdb\u884c\u4e86\u6df1\u5165\u7684\u7efc\u5408\u5b9e\u8bc1\u7814\u7a76\u3002\u7814\u7a76\u5458\u4eec\u5206\u6790\u4e86400\u4e2a\u771f\u5b9e\u7684\u4f4e\u5229\u7528\u7387\u4f5c\u4e1a\uff08\u5019\u9009\u4f5c\u4e1a\u7efc\u5408 GPU \u5229\u7528\u7387\u5c0f\u4e8e\u6216\u7b49\u4e8e50%\uff09\uff0c\u5e76\u5728\u6837\u672c\u4f5c\u4e1a\u4e2d\u53d1\u73b0\u4e86706\u4e2a\u4f4e\u5229\u7528\u7387\u95ee\u9898\uff0c\u5176\u4e2d\u5927\u90e8\u5206\u95ee\u9898\u90fd\u5f52\u56e0\u4e8e\u811a\u672c\u6216\u7a0b\u5e8f\u4e2d\u7684\u4ee3\u7801\u903b\u8f91\u95ee\u9898\u3002\u7814\u7a76\u5458\u4eec\u8fd8\u8fdb\u4e00\u6b65\u8c03\u67e5\u4e86\u8fd9\u4e9b\u95ee\u9898\u7684\u5e38\u89c1\u6839\u672c\u539f\u56e0\uff08\u5171\u56db\u5927\u7c7b\uff0c\u5341\u4e94\u4e2a\u5b50\u7c7b\uff09\uff0c\u5e76\u63d0\u51fa\u4e86\u76f8\u5e94\u7684\u4fee\u590d\u5efa\u8bae\u3002<\/p>\n\n\n\n<p>\u7814\u7a76\u7684\u4e3b\u8981\u53d1\u73b0\u5305\u62ec\uff1a<\/p>\n\n\n\n<p>\uff081\uff09\u6df1\u5ea6\u5b66\u4e60\u4f5c\u4e1a\u7684\u4f4e GPU \u5229\u7528\u7387\u6e90\u4e8e\u4e0d\u5145\u8db3\u7684 GPU \u8ba1\u7b97\u4ee5\u53ca\u975e GPU 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\u4e4b\u95f4\u7684\u6301\u7eed\u6570\u636e\u4ea4\u6362\uff087.08%\uff09\uff0c\u6570\u636e\u9884\u5904\u7406\uff083.97%\uff09\u7b49\u3002\u53ef\u91c7\u7528\u5f02\u6b65\u8bfb\u53d6\u3001\u51cf\u5c11\u901a\u4fe1\u9891\u6b21\u3001\u5f02\u6b65\u4e0a\u4f20\u6570\u636e\u3001\u5c06\u6570\u636e\u5904\u7406\u5206\u79bb\u5230\u6570\u636e\u5904\u7406\u4efb\u52a1\u3001\u6d41\u6c34\u7ebf\u8bfb\u53d6\u7b49\u65b9\u5f0f\u5feb\u901f\u4fee\u590d\u76f8\u5e94\u95ee\u9898\u3002<\/p>\n\n\n\n<p>\uff083\uff0945.18%\u7684\u95ee\u9898\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u76f8\u5173\uff0c\u5e76\u5728\u6a21\u578b\u8bad\u7ec3\u548c\u6d4b\u8bd5\u9636\u6bb5\u5c31\u66b4\u9732\u51fa\u6765\u4e86\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u4e0d\u5408\u9002\u7684\u6279\u5c3a\u5bf8\u5927\u5c0f\uff0825.64%\uff09\uff0c\u53ef\u901a\u8fc7\u5728\u4fdd\u8bc1 GPU \u663e\u5b58\u4e0d\u6ea2\u51fa\u7684\u524d\u63d0\u4e0b\u8c03\u6574\u6279\u5c3a\u5bf8\u6765\u89e3\u51b3\uff1b\u6267\u884c\u4f4e\u6548\u7684\u6a21\u578b\u68c0\u67e5\u70b9\u64cd\u4f5c\uff0816.43%\uff09\uff0c\u53ef\u91c7\u7528\u5f02\u6b65\u6267\u884c\u7684\u65b9\u5f0f\u8fdb\u884c\u4fee\u590d\uff0c\u8ba9 GPU \u548c I\/O \u8bbe\u5907\u5e76\u884c\u5de5\u4f5c\uff1bGPU \u663e\u5b58\u4e0d\u8db3\u9020\u6210\u65e0\u6cd5\u652f\u6301\u66f4\u5927\u89c4\u6a21\u7684 GPU \u8ba1\u7b97\uff083.12%\uff09\uff0c\u53ef\u901a\u8fc7\u7533\u8bf7\u66f4\u591a\u8ba1\u7b97\u8d44\u6e90\u6216\u8005\u91c7\u7528\u5408\u7406\u7684\u6570\u636e\u653e\u7f6e\u7b56\u7565\uff0c\u4ee5\u53ca\u65f6\u6362\u51fa\u51b7\u6570\u636e\u6765\u89e3\u51b3\u3002<\/p>\n\n\n\n<p>\uff084\uff09\u4e00\u90e8\u5206\u4f4e\u5229\u7528\u7387\u95ee\u9898\u662f\u7531\u4e0d\u5408\u9002\u7684\u4f5c\u4e1a\u7c7b\u578b\u6216\u914d\u7f6e\uff084.82%\uff09\u53ca\u4f9d\u8d56\u5e93\u548c\u6846\u67b6\u9519\u8bef\u4f7f\u7528\u95ee\u9898\uff083.97%\uff09\u5f15\u8d77\u7684\u3002\u4f8b\u5982\uff0c\u7528\u6237\u4ea4\u4e92\u5f0f\u5730\u64cd\u4f5c GPU\uff082.12%\uff09\uff1b\u6709\u4e9b\u7528\u6237\u8d85\u989d\u7533\u8bf7 GPU\uff080.85%\uff09\uff0c\u9020\u6210 GPU \u90e8\u5206\u95f2\u7f6e\uff1b\u4e00\u4e9b\u7528\u6237\u63d0\u4ea4\u4e86\u672c\u4e0d\u9700\u8981\u4f7f\u7528 GPU \u7684\u6570\u636e\u5904\u7406\u4efb\u52a1\uff080.57%\uff09\uff1b\u8fd8\u6709\u4e9b\u7528\u6237\u7684\u7a0b\u5e8f\u8fdd\u53cd\u4e86\u7cfb\u7edf\u548c\u4e0a\u4e0b\u6587\u7ea6\u5b9a\uff0c\u9020\u6210 API \u8bef\u7528\uff082.27%\uff09\uff0c\u4ece\u800c\u672a\u80fd\u5145\u5206\u4f7f\u7528 GPU \u8d44\u6e90\u3002<\/p>\n\n\n\n<p>\uff085\uff09\u5927\u591a\u6570\uff0884.99%\uff09\u4f4e GPU \u5229\u7528\u7387\u95ee\u9898\u53ef\u4ee5\u901a\u8fc7\u5c11\u91cf\u4ee3\u7801\u6216\u811a\u672c\u7684\u4fee\u6539\u6765\u89e3\u51b3\u3002\u56e0\u6b64\uff0c\u7814\u7a76\u5458\u4eec\u8bbe\u8ba1\u4e86\u5b9e\u7528\u6027\u5f3a\u4e14\u6613\u4e8e\u5b9e\u73b0\u548c\u9a8c\u8bc1\u7684\u4fee\u590d\u65b9\u6cd5\uff0c\u7528\u6237\u53ef\u4ee5\u5feb\u901f\u6539\u8fdb\u4f5c\u4e1a\u3001\u63d0\u5347 GPU \u5229\u7528\u7387\u3002\u7814\u7a76\u5458\u4eec\u5bf9 BERT \u548c Swin Transformer \u8fd9\u4e24\u4e2a\u5178\u578b\u4f5c\u4e1a\u8fdb\u884c\u4e86\u4fee\u590d\uff0c\u5b9e\u9a8c\u7ed3\u679c\u663e\u793a\u5b83\u4eec\u5206\u522b\u53d6\u5f97\u4e86\u9ad8\u8fbe7.52\u500d\u548c3.95\u500d\u7684\u6027\u80fd\u63d0\u5347\u3002<\/p>\n\n\n\n<p>\u57fa\u4e8e\u4ee5\u4e0a\u7684\u5b9e\u8bc1\u7814\u7a76\u7ed3\u679c\uff0c\u7814\u7a76\u5458\u4eec\u6307\u51fa\u4e86\u4ee5\u4e0b\u672a\u6765\u7814\u7a76\u7684\u53ef\u80fd\u65b9\u5411\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5de5\u5177\u652f\u6301\uff1aGPU \u5229\u7528\u7387\u7684\u9884\u4f30\u548c\u9884\u6d4b\u3001\u4ee3\u7801\u68c0\u67e5\u5de5\u5177\u3001\u9ad8\u6548\u6a21\u578b\u68c0\u67e5\u70b9\u3002<\/li>\n\n\n\n<li>\u5e73\u53f0\u63d0\u5347\uff1a\u5f02\u6784\u6d41\u6c34\u7ebf\u3001GPU \u5171\u4eab\u3001\u5206\u5e03\u5f0f\u6570\u636e\u7f13\u5b58\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u6211\u4eec\u7684\u7814\u7a76\u4e3a\u63d0\u5347\u6df1\u5ea6\u5b66\u4e60\u4f5c\u4e1a\u548c\u5e73\u53f0\u7684 GPU \u5229\u7528\u7387\u63d0\u4f9b\u4e86\u5b9d\u8d35\u7684\u5efa\u8bae\u3002\u901a\u8fc7\u63a2\u8ba8\u6df1\u5ea6\u5b66\u4e60\u4f5c\u4e1a\u7684\u5f00\u53d1\u548c\u4fee\u590d\uff08\u4e24\u4e2a\u65b9\u9762\uff09\uff0c\u8fd9\u9879\u5de5\u4f5c\u8fdb\u4e00\u6b65\u542f\u793a\u4e86\u53ef\u80fd\u7684\u7814\u7a76\u65b9\u5411\u548c\u5de5\u5177\u652f\u6301\u3001\u4e3a\u6df1\u5ea6\u5b66\u4e60\u7cfb\u7edf\u548c\u5e73\u53f0\u7684\u8bbe\u8ba1\u4e0e\u7ba1\u7406\u63d0\u4f9b\u4e86\u66f4\u597d\u7684\u6307\u5bfc\uff0c\u4ece\u800c\u5e2e\u52a9\u7cfb\u7edf\u5f00\u53d1\u5de5\u7a0b\u5e08\u548c\u7b97\u6cd5\u5de5\u7a0b\u5e08\u89e3\u51b3\u4f4e\u5229\u7528\u7387\u95ee\u9898\u5e76\u63d0\u5347\u5f00\u53d1\u6548\u7387\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"research2\">KPDDS\uff1a\u901a\u8fc7\u5173\u952e\u70b9\u9a71\u52a8\u7684\u6570\u636e\u5408\u6210\u89e3\u51b3\u6570\u5b66\u95ee\u9898<\/h3>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"218\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-3-768x218-1.png\" alt=\"Key-point-driven Data Synthesis with its Enhancement on Mathematical Reasoning\" class=\"wp-image-1075923\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-3-768x218-1.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-3-768x218-1-300x85.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-3-768x218-1-240x68.png 240w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<p>\u8bba\u6587\u94fe\u63a5\uff1a<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2403.02333\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/arxiv.org\/abs\/2403.02333<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n<p>\u5c3d\u7ba1\u5927\u8bed\u8a00\u6a21\u578b\uff08LLMs\uff09\u5728\u63a8\u7406\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u8272\uff0c\u4f46\u5728\u9762\u5bf9\u590d\u6742\u7684\u6570\u5b66\u95ee\u9898\u65f6\uff0c\u8fd9\u4e9b\u6a21\u578b\u4f9d\u7136\u6709\u4e00\u5b9a\u7684\u5c40\u9650\u6027\u3002\u8fd9\u4e3b\u8981\u662f\u56e0\u4e3a\u89e3\u51b3\u6570\u5b66\u95ee\u9898\u4e0d\u4ec5\u8981\u7406\u89e3\u95ee\u9898\u672c\u8eab\uff0c\u8fd8\u9700\u8981\u6267\u884c\u4e00\u8fde\u4e32\u7684\u903b\u8f91\u548c\u6570\u5b66\u64cd\u4f5c\uff0c\u5bf9\u6a21\u578b\u7684\u63a8\u7406\u548c\u8ba1\u7b97\u80fd\u529b\u63d0\u51fa\u4e86\u8f83\u9ad8\u7684\u8981\u6c42\u3002\u9274\u4e8e\u5f53\u524d\u7684\u6570\u5b66\u95ee\u9898\u6570\u636e\u96c6\u89c4\u6a21\u8f83\u5c0f\uff0c\u9650\u5236\u4e86\u6a21\u578b\u5fae\u8c03\u7684\u6548\u679c\uff0c\u4f17\u591a\u7814\u7a76\u805a\u7126\u4e8e\u751f\u6210\u66f4\u5927\u89c4\u6a21\u7684\u6570\u5b66\u6307\u4ee4\u6570\u636e\u96c6\u3002\u79d1\u7814\u4eba\u5458\u5c1d\u8bd5\u5bf9\u73b0\u6709\u6570\u636e\u96c6\u8fdb\u884c\u6539\u5199\u548c\u6269\u5c55\uff0c\u6216\u662f\u57fa\u4e8e\u73b0\u6709\u6570\u5b66\u77e5\u8bc6\u5e93\u5408\u6210\u5168\u65b0\u7684\u95ee\u9898\uff0c\u4f46\u8fd9\u4e9b\u65b9\u6cd5\u751f\u6210\u7684\u6570\u636e\u5728\u591a\u6837\u6027\u548c\u5408\u7406\u6027\u65b9\u9762\u5f80\u5f80\u4e0d\u5c3d\u4eba\u610f\u3002<\/p>\n\n\n\n<p>\u9488\u5bf9\u8fd9\u4e00\u95ee\u9898\uff0c\u672c\u6587\u5f15\u5165\u4e86\u4e00\u79cd\u6570\u636e\u5408\u6210\u65b0\u8303\u5f0f\u2014\u2014\u5173\u952e\u70b9\u9a71\u52a8\u7684\u6570\u636e\u5408\u6210\uff08KPDDS\uff09\uff0c\u65e8\u5728\u901a\u8fc7\u7406\u89e3\u548c\u5e94\u7528\u6570\u5b66\u95ee\u9898\u7684\u6838\u5fc3\u6982\u5ff5\u6765\u5408\u6210\u8bad\u7ec3\u6570\u636e\u3002\u5176\u5173\u952e\u5728\u4e8e KPDDS \u80fd\u591f\u4ece\u771f\u5b9e\u6570\u636e\u96c6\u4e2d\u63d0\u53d6\u77e5\u8bc6\uff0c\u5e76\u5229\u7528\u89e3\u9898\u7684\u5173\u952e\u70b9\u53ca\u793a\u4f8b\u6765\u751f\u6210\u65b0\u95ee\u9898\u3002\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u80fd\u4fdd\u8bc1\u4e86\u5408\u6210\u6570\u636e\u7684\u8d28\u91cf\u548c\u53ef\u63a7\u6027\uff0c\u8fd8\u80fd\u591f\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u6a21\u62df\u771f\u5b9e\u6570\u636e\u7684\u5206\u5e03\uff0c\u4e3a\u5927\u8bed\u8a00\u6a21\u578b\u7684\u8bad\u7ec3\u63d0\u4f9b\u4e86\u66f4\u4e3a\u4e30\u5bcc\u4e14\u51c6\u786e\u7684\u6570\u636e\u8d44\u6e90\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"568\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-4-1024x568-1.png\" alt=\"Key-point-driven Data Synthesis with its Enhancement on Mathematical Reasoning | diagram\" class=\"wp-image-1075926\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-4-1024x568-1.png 1024w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-4-1024x568-1-300x166.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-4-1024x568-1-768x426.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-4-1024x568-1-240x133.png 240w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">\u56fe2\uff1aKPDDS \u6d41\u7a0b\u56fe<\/figcaption><\/figure>\n\n\n\n<p>KPDDS \u7684\u7b2c\u4e00\u9636\u6bb5\u77e5\u8bc6\u6784\u5efa\uff0c\u7531\u77e5\u8bc6\u63d0\u53d6\u548c\u4e3b\u9898\u5171\u73b0\u6982\u7387\u77e9\u9635\u6784\u5efa\u4e24\u90e8\u5206\u7ec4\u6210\u3002\u5728\u77e5\u8bc6\u63d0\u53d6\u90e8\u5206\uff0cKPDDS \u4f7f\u7528 GPT-4 \u4ece\u79cd\u5b50\u95ee\u9898\u4e2d\u63d0\u53d6\u89e3\u51b3\u95ee\u9898\u6240\u9700\u7684\u5173\u952e\u77e5\u8bc6\uff0c\u5e76\u5c06\u77e5\u8bc6\u5206\u4e3a\u4e3b\u9898\uff08Topic\uff09\u548c\u5173\u952e\u70b9\uff08Key Points\uff09\u4e24\u4e2a\u5c42\u6b21\u3002\u4e3a\u8fdb\u4e00\u6b65\u5904\u7406\u4ece\u79cd\u5b50\u95ee\u9898\u4e2d\u63d0\u53d6\u7684\u77e5\u8bc6\u6570\u636e\uff0c\u7814\u7a76\u5458\u4eec\u901a\u8fc7\u8ba1\u7b97\u4e3b\u9898\u7684 embedding \u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6\u8fdb\u884c\u53bb\u91cd\u548c\u805a\u7c7b\uff0c\u6700\u7ec8\u6784\u5efa\u4e86\u201c\u6570\u5b66\u5173\u952e\u70b9\u53ca\u7ec3\u4e60\u201d\uff08MPKP\uff09\u6570\u636e\u96c6\u3002\u5728\u4e3b\u9898\u5171\u73b0\u6982\u7387\u77e9\u9635\u6784\u5efa\u90e8\u5206\uff0cKPDDS \u4ece MPKP \u6570\u636e\u96c6\u4e2d\u7684\u6570\u5b66\u95ee\u9898\u4e3b\u9898\u6784\u5efa\u4e86\u4e3b\u9898\u5171\u73b0\u6982\u7387\u77e9\u9635\uff08TCPM\uff09\uff0c\u6765\u91cf\u5316\u6570\u636e\u96c6\u5185\u4e3b\u9898\u95f4\u7684\u5171\u73b0\u6982\u7387\uff0c\u4ece\u800c\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u7406\u89e3\u590d\u6742\u7684\u7ed3\u6784\u3002<\/p>\n\n\n\n<p>\u7b2c\u4e8c\u9636\u6bb5\u7ec3\u4e60\u5408\u6210\uff0c\u7531\u95ee\u9898\u751f\u6210\u4e0e\u8bc4\u5206\uff0c\u548c\u5171\u8bc6\u89e3\u7b54\u4e24\u90e8\u5206\u7ec4\u6210\u3002\u5728\u95ee\u9898\u751f\u6210\u4e0e\u8bc4\u5206\u65b9\u9762\uff0cKPDDS \u6839\u636e TCPM \u6267\u884c\u4e3b\u9898\u7684\u6982\u7387\u6027\u91c7\u6837\uff0c\u6784\u5efa\u5173\u952e\u70b9-\u7ec3\u4e60\u4fe1\u606f\u96c6\u3002\u63a5\u7740\uff0c\u518d\u4f7f\u7528 GPT-4 \u57fa\u4e8e\u4fe1\u606f\u96c6\u751f\u6210\u65b0\u95ee\u9898\uff0c\u5e76\u901a\u8fc7\u6253\u5206\u91cf\u5316\u8bc4\u4f30\u6765\u786e\u5b9a\u6bcf\u4e2a\u95ee\u9898\u7684\u8d28\u91cf\uff0c\u6700\u540e\u4ec5\u4fdd\u7559\u9ad8\u4e8e\u9608\u503c\u7684\u95ee\u9898\u7528\u4e8e\u4e0b\u4e00\u6b65\u5408\u6210\u3002\u4e3a\u4e86\u51cf\u5c11\u566a\u58f0\u6570\u636e\u7684\u5f71\u54cd\u5e76\u589e\u5f3a\u7b54\u6848\u751f\u6210\u8fc7\u7a0b\u7684\u53ef\u9760\u6027\uff0cKPDDS \u91c7\u7528\u4e86\u6295\u7968\u5171\u8bc6\u65b9\u6cd5\u751f\u6210\u65b0\u95ee\u9898\u7684\u89e3\u7b54\u3002\u7814\u7a76\u5458\u4eec\u5728\u6295\u7968\u9636\u6bb5\u91c7\u7528\u4e86 sympy \u7b49\u5de5\u5177\u5305\uff0c\u4ee5\u786e\u4fdd\u751f\u6210\u7b54\u6848\u7684\u6b63\u786e\u6027\uff0c\u5373\u4f7f\u662f\u4ee5\u4e0d\u540c\u5f62\u5f0f\uff08\u5982\u5206\u6570\u548c\u5c0f\u6570\uff09\u51fa\u73b0\u7684\u7b49\u6548\u7b54\u6848\u4e5f\u80fd\u88ab\u8ba4\u5b9a\u4e3a\u76f8\u540c\u3002\u5bf9\u5171\u8bc6\u7b56\u7565\u8fdb\u884c\u7684\u6d88\u878d\u5b9e\u9a8c\u8bc1\u660e\u4e86\u8be5\u65b9\u6cd5\u7684\u6709\u6548\u6027\uff0c\u5e76\u786e\u5b9a\u4e86\u6700\u4f73\u9608\u503c\u4ee5\u8fc7\u6ee4\u6570\u636e\u3002<\/p>\n\n\n\n<p>\u6b64\u5916\uff0c\u7814\u7a76\u5458\u4eec\u8fd8\u6784\u5efa\u4e86 KPMATH-Plus \u6570\u636e\u96c6\uff0c\u7531 KPMATH-M (252K)\u3001KPMATH-G (613K) \u548c MixMath (711K) \u4e09\u90e8\u5206\u7ec4\u6210\uff0c\u5171\u5305\u542b1,576K\u4e2a\u6837\u672c\uff0c\u6db5\u76d6\u4e86\u4e30\u5bcc\u591a\u6837\u7684\u6570\u5b66\u95ee\u9898\u3002<\/p>\n\n\n\n<p>\u7814\u7a76\u5458\u4eec\u4f7f\u7528 KPMath-Plus \u6570\u636e\u96c6\u5bf9 Mistral-7b\u3001DeepSeekMath-7b\u3001Llama-2-13b\u3001Llemma-34b \u6a21\u578b\u8fdb\u884c\u5fae\u8c03\uff0c\u5747\u5e26\u6765\u663e\u8457\u63d0\u5347\u3002KPMath-Plus-DeepSeekMath \u5728\u516d\u4e2a\u5e38\u7528\u7684\u6570\u5b66\u8bc4\u4f30\u6570\u636e\u96c6\u4e0a\u83b7\u5f97\u4e86\u6700\u4f73\u6027\u80fd\uff0c\u8d85\u8d8a\u4e867B\u81f370B\u8303\u56f4\u5185\u7684\u5176\u4ed6\u6a21\u578b\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u5728\u5308\u7259\u5229\u8003\u8bd5\u4e2d\uff0cKPMath-Plus-Mistral-7B \u7684\u6210\u7ee9\u4ec5\u6b21\u4e8e GPT-4 \u548c Grok-1\uff0c\u4e0e\u5176\u4ed6\u5fae\u8c03\u6a21\u578b\u76f8\u6bd4\uff0c\u5728\u5308\u7259\u5229\u8003\u8bd5\u548c GSM8K \u6d4b\u8bd5\u4e2d\u5c55\u73b0\u51fa\u4e86\u5747\u8861\u7684\u6027\u80fd\u3002\u8fd9\u8bc1\u660e\u4e86 KPMath-Plus \u6570\u636e\u96c6\u7684\u6709\u6548\u6027\uff0c\u5b83\u4e0d\u4ec5\u80fd\u591f\u901a\u8fc7\u5fae\u8c03\u63d0\u5347\u6a21\u578b\u6027\u80fd\uff0c\u4e5f\u80fd\u786e\u4fdd\u6a21\u578b\u89e3\u9898\u80fd\u529b\u7684\u5e7f\u6cdb\u9002\u7528\u6027\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"925\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-5-1024x925-1.png\" alt=\"Key-point-driven Data Synthesis with its Enhancement on Mathematical Reasoning | table\" class=\"wp-image-1075929\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-5-1024x925-1.png 1024w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-5-1024x925-1-300x271.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-5-1024x925-1-768x694.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-5-1024x925-1-199x180.png 199w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">\u88681\uff1a\u5728\u516d\u4e2a\u6570\u5b66\u63a8\u7406\u4efb\u52a1\u4e0a\u7684\u7ed3\u679c<\/figcaption><\/figure>\n\n\n\n<p>KPDDS \u63d0\u4f9b\u4e86\u4e00\u79cd\u6570\u636e\u5408\u6210\u7684\u65b0\u8303\u5f0f\u6765\u589e\u5f3a LLMs \u5904\u7406\u6570\u5b66\u95ee\u9898\u7684\u80fd\u529b\uff0c\u8fd9\u662f\u5728\u63a2\u7d22\u590d\u6742\u63a8\u7406\u95ee\u9898\u65b9\u9762\u7684\u521d\u6b65\u5c1d\u8bd5\u3002\u672a\u6765\uff0c\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662\u7814\u7a76\u5458\u4eec\u7684\u7814\u7a76\u89c6\u91ce\u5c06\u4e0d\u4ec5\u5c40\u9650\u4e8e\u6570\u5b66\uff0c\u8fd8\u4f1a\u6269\u5c55\u5230\u66f4\u5e7f\u6cdb\u7684\u5b66\u79d1\u4e2d\uff0c\u901a\u8fc7\u5229\u7528\u8de8\u5b66\u79d1\u77e5\u8bc6\uff0c\u5f15\u5165\u66f4\u4e30\u5bcc\u548c\u590d\u6742\u7684\u7406\u8bba\u4e0e\u6982\u5ff5\uff0c\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u8bad\u7ec3\u6570\u636e\uff0c\u8fdb\u4e00\u6b65\u63d0\u9ad8\u6a21\u578b\u7684\u63a8\u7406\u548c\u89e3\u9898\u80fd\u529b\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"research3\">MathScale\uff1a\u5927\u89c4\u6a21\u5408\u6210\u6570\u5b66\u63a8\u7406\u7684\u6307\u4ee4\u5fae\u8c03\u6570\u636e<\/h3>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"706\" height=\"153\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-6.png\" alt=\"MathScale: Scaling Instruction Tuning for Mathematical Reasoning\" class=\"wp-image-1075932\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-6.png 706w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-6-300x65.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-6-240x52.png 240w\" sizes=\"auto, (max-width: 706px) 100vw, 706px\" \/><\/figure>\n\n\n\n<p>\u8bba\u6587\u94fe\u63a5\uff1a<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2403.02884\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/arxiv.org\/abs\/2403.02884<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n<p>GitHub\u94fe\u63a5\uff1a<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/github.com\/microsoft\/unilm\/tree\/master\/mathscale\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/github.com\/microsoft\/unilm\/tree\/master\/mathscale<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n<p>\u6307\u4ee4\u5fae\u8c03\uff08instruction tuning\uff09\u662f\u4e00\u79cd\u6709\u6548\u63d0\u9ad8\u5927\u8bed\u8a00\u6a21\u578b\uff08LLMs\uff09\u67d0\u4e9b\u80fd\u529b\u7684\u65b9\u5f0f\uff0c\u4f46\u76ee\u524d\u80fd\u591f\u7528\u4e8e\u63d0\u9ad8\u6570\u5b66\u63a8\u7406\u7684\u9ad8\u8d28\u91cf\u6307\u4ee4\u5fae\u8c03\u6570\u636e\u5341\u5206\u6709\u9650\uff08\u5982 GSM8K \u548c MATH\uff09\u3002\u56e0\u6b64\uff0c\u80fd\u591f\u5927\u89c4\u6a21\u5408\u6210\u9ad8\u8d28\u91cf\u7684\u6570\u5b66\u63a8\u7406\u6307\u4ee4\u5fae\u8c03\u6570\u636e\u5bf9\u4e8e\u63d0\u9ad8 LLMs \u7684\u6570\u5b66\u80fd\u529b\u975e\u5e38\u91cd\u8981\u3002<\/p>\n\n\n\n<p>\u73b0\u6709\u7684\u5408\u6210\u6570\u5b66\u63a8\u7406\u6570\u636e\u7684\u65b9\u6cd5\u4e3b\u8981\u662f\u5bf9\u5df2\u6709\u6570\u636e\u96c6\u8fdb\u884c\u6570\u636e\u6269\u5c55\uff08data argumentation\uff09\u3002\u8fd9\u4e9b\u65b9\u6cd5\u4ea7\u751f\u7684\u6570\u5b66\u63a8\u7406\u6570\u636e\u4f1a\u548c\u5df2\u6709\u7684\u6570\u636e\u96c6\u975e\u5e38\u76f8\u4f3c\uff0c\u56e0\u6b64\u96be\u4ee5\u6269\u5c55\u5230\u66f4\u5927\u89c4\u6a21\u3002\u5bf9\u6b64\uff0c\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662\u7684\u7814\u7a76\u5458\u4eec\u63d0\u51fa\u4e86 MathScale \u65b9\u6cd5\uff0c\u6d41\u7a0b\u5982\u56fe3\u6240\u793a\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u9996\u5148\u5229\u7528 frontier LLM\uff08\u5982GPT-3.5\uff09\u4ece\u5df2\u6709\u7684\u79cd\u5b50\u6570\u5b66\u95ee\u9898\u63d0\u53d6 high level \u7684\u6982\u5ff5\uff08\u5373\u4e3b\u9898\u548c\u77e5\u8bc6\u70b9\uff09\u3002\u7136\u540e\uff0cMathScale \u5229\u7528\u5df2\u7ecf\u63d0\u53d6\u7684\u4e3b\u9898\u548c\u77e5\u8bc6\u70b9\u5efa\u7acb\u4e00\u4e2a\u6982\u5ff5\u56fe\u3002\u8fd9\u4e2a\u6982\u5ff5\u56fe\u4e2d\u7684\u8fb9\u7684\u6743\u91cd\u662f\u901a\u8fc7\u4e3b\u9898\u4e0e\u4e3b\u9898\u3001\u4e3b\u9898\u4e0e\u77e5\u8bc6\u70b9\u6216\u8005\u77e5\u8bc6\u70b9\u4e0e\u77e5\u8bc6\u70b9\u4e4b\u95f4\u7684\u5171\u73b0\u4fe1\u606f\u5f97\u5230\u7684\u3002\u63a5\u4e0b\u6765\uff0c\u901a\u8fc7\u5728\u5efa\u7acb\u7684\u6982\u5ff5\u56fe\u4e0a\u8fdb\u884c\u968f\u673a\u6e38\u8d70\uff0c\u91c7\u6837\u51fa\u4e3b\u9898\u548c\u77e5\u8bc6\u70b9\u7684\u7ec4\u5408\uff0c\u5e76\u518d\u6b21\u5229\u7528 GPT-3.5 \u57fa\u4e8e\u91c7\u6837\u51fa\u7684\u4e3b\u9898\u53ca\u77e5\u8bc6\u70b9\u751f\u6210\u65b0\u7684\u95ee\u9898\u548c\u7b54\u6848\u3002\u5728\u6982\u5ff5\u56fe\u4e0a\u7684\u968f\u673a\u6e38\u8d70\u7b97\u6cd5\u53ef\u4ee5\u521b\u9020\u51fa\u591a\u6837\u7684\u4e3b\u9898\u548c\u77e5\u8bc6\u70b9\u7ec4\u5408\uff0c\u4ece\u800c\u4fdd\u8bc1\u751f\u6210\u6570\u5b66\u63a8\u7406\u6570\u636e\u7684\u591a\u6837\u6027\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"820\" height=\"345\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-7.png\" alt=\"MathScale: Scaling Instruction Tuning for Mathematical Reasoning | diagram\" class=\"wp-image-1075935\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-7.png 820w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-7-300x126.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-7-768x323.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-7-240x101.png 240w\" sizes=\"auto, (max-width: 820px) 100vw, 820px\" \/><figcaption class=\"wp-element-caption\">\u56fe3\uff1aMathScale \u5408\u6210\u6570\u5b66\u63a8\u7406\u6570\u636e\u7684\u6d41\u7a0b\u56fe<\/figcaption><\/figure>\n\n\n\n<p>\u672c\u7814\u7a76\u7528 MathScale \u65b9\u6cd5\u751f\u6210\u4e86\u4e24\u767e\u4e07\u7684\u6570\u5b66\u6307\u4ee4\u6570\u636e\u96c6\uff08\u5373MathScaleQA\uff09\u3002\u7814\u7a76\u5458\u4eec\u7528 MathScaleQA \u5fae\u8c03\u4e86 Mistral-7B\uff0cLlama2-7B \u53ca Llama2-13B\uff0c\u5f97\u5230\u7684\u6a21\u578b\u5728\u6570\u5b66\u80fd\u529b\u4e0a\u8d85\u8d8a\u5df2\u6709\u65b9\u6cd5\uff08\u5728 MWPBench \u7684\u5341\u4e2a\u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u7ed3\u679c\u89c1\u88682\uff09\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"668\" height=\"427\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-8.png\" alt=\"MathScale: Scaling Instruction Tuning for Mathematical Reasoning | models comparison table\" class=\"wp-image-1075938\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-8.png 668w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-8-300x192.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-8-240x153.png 240w\" sizes=\"auto, (max-width: 668px) 100vw, 668px\" \/><figcaption class=\"wp-element-caption\">\u88682\uff1a\u5728 MWPBench \u7684\u5341\u4e2a\u6570\u636e\u96c6\u4e0a\u7684\u5b9e\u9a8c\u7ed3\u679c<\/figcaption><\/figure>\n\n\n\n<p>\u7814\u7a76\u5458\u4eec\u5728\u8be5\u7814\u7a76\u4e2d\u89c2\u5bdf\u5230\uff0c\u91c7\u7528 MathScale \u65b9\u6cd5\u751f\u6210\u7684\u6570\u636e\u96c6\u5c55\u73b0\u51fa\u4e86\u5f88\u597d\u7684 Scaling \u7279\u6027\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u968f\u7740\u751f\u6210\u8bad\u7ec3\u6570\u636e\u91cf\u7684\u589e\u52a0\uff0c\u6a21\u578b\u5728 MWPBench \u7684\u5341\u4e2a\u8bc4\u6d4b\u96c6\u4e0a\u7684\u8868\u73b0\u5448\u73b0\u51fa\u8fd1\u4f3c\u5bf9\u6570\u589e\u957f\u7684\u8d8b\u52bf\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"674\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-9.png\" alt=\"MathScale: Scaling Instruction Tuning for Mathematical Reasoning | twelve line charts\" class=\"wp-image-1075941\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-9.png 900w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-9-300x225.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-9-768x575.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-9-80x60.png 80w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-9-240x180.png 240w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><figcaption class=\"wp-element-caption\">\u56fe4\uff1aMathScale \u5728\u4e0d\u540c\u89c4\u6a21\u5408\u6210\u6570\u636e\u4e0b\u7684\u8868\u73b0<\/figcaption><\/figure>\n\n\n\n<p>\u8be5\u7279\u6027\u4e3a\u672a\u6765\u8fdb\u884c\u66f4\u5927\u89c4\u6a21\u7684\u6570\u636e\u751f\u6210\u4ee5\u53ca\u4f7f\u7528\u66f4\u5f3a frontier LLM\uff08\u5982GPT-4\uff09\u8fdb\u884c\u6570\u636e\u751f\u6210\u63d0\u4f9b\u4e86\u57fa\u7840\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"research4\">RecAI\uff1a\u5927\u6a21\u578b\u6539\u8fdb\u63a8\u8350\u7cfb\u7edf\u7684\u4e94\u79cd\u65b9\u5f0f<\/h3>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"261\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-10-768x261-1.png\" alt=\"RecAI: Leveraging Large Language Models for Next-generation Recommender Systems\" class=\"wp-image-1075944\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-10-768x261-1.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-10-768x261-1-300x102.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-10-768x261-1-240x82.png 240w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<p>\u8bba\u6587\u94fe\u63a5\uff1a<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2403.06465\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/arxiv.org\/abs\/2403.06465<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n<p>\u9879\u76ee\u94fe\u63a5\uff1a<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/github.com\/microsoft\/recai\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/github.com\/microsoft\/recai<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n<p>\u63a8\u8350\u7cfb\u7edf\u4f1a\u57fa\u4e8e\u7528\u6237\u884c\u4e3a\u6765\u63a8\u6d4b\u5176\u504f\u597d\uff0c\u4ece\u800c\u5b9e\u73b0\u5185\u5bb9\u7684\u7cbe\u51c6\u63a8\u9001\uff0c\u4f46\u73b0\u6709\u7684\u4e3b\u6d41\u63a8\u8350\u7b97\u6cd5\u5bf9\u4e8e\u7528\u6237\u800c\u8a00\u901a\u5e38\u662f\u88ab\u52a8\u7684\uff0c\u4e14\u9762\u4e34\u8bf8\u591a\u6311\u6218\uff0c\u5982\u53ef\u4ea4\u4e92\u6027\u3001\u53ef\u89e3\u91ca\u6027\u3001\u53ef\u63a7\u6027\u7b49\u3002\u5927\u8bed\u8a00\u6a21\u578b\u7684\u8bde\u751f\u4e3a\u89e3\u51b3\u8fd9\u4e9b\u6311\u6218\u5e26\u6765\u4e86\u5168\u65b0\u7684\u673a\u9047\u3002\u56e0\u6b64\uff0c\u5927\u6a21\u578b\u4e0e\u63a8\u8350\u7cfb\u7edf\u7684\u7ed3\u5408\u6210\u4e3a\u4e86\u4e1a\u754c\u7099\u624b\u53ef\u70ed\u7684\u8bdd\u9898\u3002<\/p>\n\n\n\n<p>\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662\u7684\u7814\u7a76\u5458\u4eec\u5728 RecAI \u4e00\u6587\u4e2d\u603b\u7ed3\u4e86\u5176\u8be5\u9886\u57df\u7684\u4e00\u7cfb\u5217\u63a2\u7d22\uff0c\u5e76\u5f00\u6e90\u4e86\u76f8\u5173\u9879\u76ee\u3002\u8be5\u7cfb\u5217\u5de5\u4f5c\u5c06\u4e8e2024\u5e745\u6708\u5728\u65b0\u52a0\u5761\u4e3e\u529e\u7684 WebConf\u201924 \u5927\u4f1a\u4e0a\u5c55\u793a\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"466\" src=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-11-1024x466-1.png\" alt=\"RecAI: Leveraging Large Language Models for Next-generation Recommender Systems | diagram\" class=\"wp-image-1075947\" srcset=\"https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-11-1024x466-1.png 1024w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-11-1024x466-1-300x137.png 300w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-11-1024x466-1-768x350.png 768w, https:\/\/newed.any0.dpdns.org\/en-us\/research\/wp-content\/uploads\/2024\/08\/new-arrival-in-research-10-11-1024x466-1-240x109.png 240w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">\u56fe5\uff1aRecAI \u7684\u76f8\u5173\u9879\u76ee<\/figcaption><\/figure>\n\n\n\n<p>\u5982\u4e0b\u662f\u7814\u7a76\u5458\u4eec\u5728\u672c\u6587\u4e2d\u68b3\u7406\u5e76\u5f00\u6e90\u7684\u5927\u6a21\u578b\u6539\u8fdb\u63a8\u8350\u7cfb\u7edf\u7684\u4e94\u79cd\u65b9\u5f0f\uff1a<\/p>\n\n\n\n<p>1. Recommender AI Agent\u3002\u9274\u4e8e\u5927\u6a21\u578b\u5728\u65f6\u6548\u6027\u548c\u9886\u57df\u77e5\u8bc6\u65b9\u9762\u7684\u4e0d\u8db3\uff0c\u5de5\u5177\u589e\u5f3a\u7684\u667a\u80fd\u4f53\u6210\u4e3a\u4e86\u5927\u6a21\u578b\u843d\u5730\u9886\u57df\u5e94\u7528\u7684\u6700\u7ecf\u5178\u8303\u5f0f\u4e4b\u4e00\u3002\u4f20\u7edf\u7684\u63a8\u8350\u6a21\u578b\u88ab\u89c6\u4e3a\u9886\u57df\u5185\u5b9a\u5236\u7684\u5de5\u5177\uff0c\u800c\u5927\u6a21\u578b\u5219\u626e\u6f14\u4e86\u5927\u8111\u4e2d\u67a2\u7684\u89d2\u8272\uff0c\u8d1f\u8d23\u4e0e\u7528\u6237\u5bf9\u8bdd\uff0c\u7406\u89e3\u7528\u6237\u610f\u56fe\uff0c\u5e76\u901a\u8fc7\u8c03\u7528\u5176\u4ed6\u5de5\u5177\u6765\u5b8c\u6210\u590d\u6742\u7684\u67e5\u8be2\u548c\u63a8\u8350\u4efb\u52a1\u3002\u4e3a\u4e86\u66f4\u52a0\u51c6\u786e\u3001\u9ad8\u6548\u5730\u5b8c\u6210\u5927\u6a21\u578b\u548c\u4f20\u7edf\u63a8\u8350\u6a21\u578b\u7684\u534f\u4f5c\uff0c\u7814\u7a76\u5458\u4eec\u6539\u8fdb\u4e86\u667a\u80fd\u4f53\u6846\u67b6\u4e2d\u7684\u4efb\u52a1\u89c4\u5212\u3001\u8bb0\u5fc6\u548c\u5de5\u5177\u5b66\u4e60\u7684\u673a\u5236\u3002<\/p>\n\n\n\n<p>2. Generative RecLM\u3002\u667a\u80fd\u4f53\u7684\u4e3b\u8981\u4e0d\u8db3\u5728\u4e8e\u53cd\u5e94\u8fdf\u7f13\uff0c\u65e0\u6cd5\u5b9e\u73b0\u6d41\u5f0f\u56de\u5e94\u3002\u8fd9\u5728\u8bb8\u591a\u5bf9\u54cd\u5e94\u901f\u5ea6\u654f\u611f\u7684\u63a8\u8350\u573a\u666f\u4e2d\u5c24\u4e3a\u7a81\u51fa\u3002\u56e0\u6b64\uff0c\u5fae\u8c03\u5927\u8bed\u8a00\u6a21\u578b\uff0c\u4f7f\u5176\u81ea\u8eab\u638c\u63e1\u9886\u57df\u77e5\u8bc6\uff0c\u7406\u89e3\u7528\u6237\u7684\u590d\u6742\u3001\u52a8\u6001\u6307\u4ee4\uff0c\u6210\u4e3a\u63d0\u5347\u5176\u6027\u80fd\u7684\u5fc5\u7ecf\u4e4b\u8def\u3002\u4e3a\u6b64\uff0c\u7814\u7a76\u5458\u4eec\u8bbe\u8ba1\u4e86\u4e00\u5957\u7ed3\u5408\u6709\u76d1\u7763\u5b66\u4e60\uff08SFT\uff09\u548c\u5f3a\u5316\u5b66\u4e60\uff08RL\uff09\u7684\u4e24\u9636\u6bb5\u5b66\u4e60\u65b9\u6cd5\uff0c\u4e0d\u4ec5\u63d0\u5347\u4e86\u5927\u6a21\u578b\u54cd\u5e94\u7528\u6237\u6307\u4ee4\u7684\u80fd\u529b\uff0c\u8fd8\u80fd\u51cf\u5c11\u5927\u6a21\u578b\u7684\u8f93\u51fa\u9519\u8bef\u3002<\/p>\n\n\n\n<p>3. Embedding-oriented RecLM\u3002\u57fa\u4e8e\u5d4c\u5165\u7684\u5339\u914d\u8303\u5f0f\uff0c\u65e0\u8bba\u662f\u5728\u63a8\u8350\u7cfb\u7edf\u8fd8\u662f\u641c\u7d22\u5f15\u64ce\uff0c\u90fd\u53d1\u6325\u7740\u5de8\u5927\u7684\u4f5c\u7528\u3002\u8bed\u8a00\u6a21\u578b\u7684\u6210\u719f\u4f7f\u5f97\u4efb\u4f55\u5f62\u5f0f\u7684\u6587\u672c\u90fd\u80fd\u8f6c\u5316\u4e3a\u6709\u6548\u7684\u5d4c\u5165\u8868\u5f81\uff0c\u4f8b\u5982\u7528\u6237\u8f93\u5165\u7684\u67e5\u8be2\u3001\u7528\u6237\u7684\u9690\u5f0f\u884c\u4e3a\u5e8f\u5217\u3001\u7528\u6237\u548c\u667a\u80fd\u4f53\u7684\u5bf9\u8bdd\u5386\u53f2\u7b49\u3002\u6839\u636e\u901a\u7528\u7684\u6587\u672c\u5339\u914d\u6a21\u578b\uff0c\u7814\u7a76\u5458\u4eec\u8bbe\u8ba1\u4e8610\u7c7b\u4efb\u52a1\uff0c\u4e13\u95e8\u6fc0\u53d1\u6a21\u578b\u5728\u7269\u54c1\u5339\u914d\u65b9\u9762\u7684\u80fd\u529b\uff0c\u4ee5\u4fbf\u7edf\u4e00\u641c\u7d22\u548c\u63a8\u8350\u53ec\u56de\u3001\u4e3a\u6392\u5e8f\u4efb\u52a1\u63d0\u4f9b\u7279\u5f81\uff0c\u4ee5\u53ca\u5728\u667a\u80fd\u4f53\u6846\u67b6\u4e2d\u4f5c\u4e3a\u57fa\u4e8e\u81ea\u7136\u8bed\u8a00\u8f93\u5165\u7684\u63a8\u8350\u5de5\u5177\u3002<\/p>\n\n\n\n<p>4. Knowledge Plugin\u3002\u5728\u5f88\u591a\u5b9e\u9645\u573a\u666f\u4e2d\uff0c\u4f8b\u5982\u4ec5\u6709 API \u670d\u52a1\u7684\u60c5\u51b5\u4e0b\uff0c\u5927\u8bed\u8a00\u6a21\u578b\u662f\u4e0d\u80fd\u8fdb\u884c\u4fee\u6539\u7684\u3002\u5728\u63d0\u793a\u8bcd\u4e2d\u52a0\u5165\u5fc5\u8981\u7684\u80cc\u666f\u77e5\u8bc6\uff0c\u4f8b\u5982\u5546\u54c1\u7684\u63cf\u8ff0\uff0c\u76f8\u4f3c\u7528\u6237\u7684\u884c\u4e3a\u6a21\u5f0f\u7b49\uff0c\u662f\u4e00\u79cd\u6709\u6548\u4e14\u5e38\u7528\u7684\u8865\u5145\u9886\u57df\u77e5\u8bc6\u7684\u624b\u6bb5\u3002\u5c3d\u7ba1\u73b0\u5728\u8bb8\u591a\u6280\u672f\u80fd\u652f\u6301\u957f\u5e8f\u5217\u5efa\u6a21\uff0c\u4f8b\u5982 GPT-4-turbo \u53ef\u4ee5\u652f\u6301128k\u7684\u957f\u6587\u672c\u8f93\u5165\uff0c\u4f46\u5b83\u4eec\u8fd8\u662f\u65e0\u6cd5\u76f4\u63a5\u5904\u7406\u63a8\u8350\u7cfb\u7edf\u7684\u6d77\u91cf\u7528\u6237\u65e5\u5fd7\u3002\u4e8e\u662f\uff0c\u7814\u7a76\u5458\u4eec\u8bbe\u8ba1\u4e86\u4e00\u5957\u65b9\u6cd5\uff0c\u901a\u8fc7\u7cbe\u7b80\u7528\u6237\u884c\u4e3a\u548c\u5546\u54c1\u56fe\u8c31\u6570\u636e\uff0c\u7528\u6700\u7ecf\u6d4e\u7684\u9014\u5f84\u4e3a\u5927\u8bed\u8a00\u6a21\u578b\u63d0\u4f9b\u9886\u57df\u77e5\u8bc6\u3002<\/p>\n\n\n\n<p>5. 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