{"id":1141,"date":"2025-07-30T16:24:00","date_gmt":"2025-07-30T08:24:00","guid":{"rendered":"http:\/\/www.applicationsofllm.com\/?p=1141"},"modified":"2025-09-17T16:26:37","modified_gmt":"2025-09-17T08:26:37","slug":"deepmind%e7%88%86%e7%81%ab%e8%ae%ba%e6%96%87%ef%bc%9a%e5%90%91%e9%87%8f%e5%b5%8c%e5%85%a5%e6%a8%a1%e5%9e%8b%e5%ad%98%e5%9c%a8%e6%95%b0%e5%ad%a6%e4%b8%8a%e9%99%90%ef%bc%8cscaling-laws%e6%94%be%e7%bc%93","status":"publish","type":"post","link":"http:\/\/www.applicationsofllm.com\/index.php\/2025\/07\/30\/deepmind%e7%88%86%e7%81%ab%e8%ae%ba%e6%96%87%ef%bc%9a%e5%90%91%e9%87%8f%e5%b5%8c%e5%85%a5%e6%a8%a1%e5%9e%8b%e5%ad%98%e5%9c%a8%e6%95%b0%e5%ad%a6%e4%b8%8a%e9%99%90%ef%bc%8cscaling-laws%e6%94%be%e7%bc%93\/","title":{"rendered":"DeepMind\u7206\u706b\u8bba\u6587\uff1a\u5411\u91cf\u5d4c\u5165\u6a21\u578b\u5b58\u5728\u6570\u5b66\u4e0a\u9650\uff0cScaling laws\u653e\u7f13\u5b9e\u9524\uff1f"},"content":{"rendered":"\n

\u8fd9\u51e0\u5929\uff0c\u4e00\u7bc7\u5173\u4e8e\u5411\u91cf\u5d4c\u5165\uff08Vector Embeddings\uff09\u5c40\u9650\u6027\u7684\u8bba\u6587\u5728 AlphaXiv \u4e0a\u7206\u706b\uff0c\u70ed\u5ea6\u98d9\u5347\u5230\u4e86\u8fd1 9000\u3002<\/p>\n\n\n\n

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\"\"<\/figure>\n\n\n\n

                \u56fe\u6e90\uff1aveaviate<\/sup><\/p>\n\n\n\n

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\u4ece GPT-2 \u5230 GPT-4\uff0c\u518d\u5230 GPT-5\uff0c\u6a21\u578b\u53c2\u6570\u91cf\u548c\u6570\u636e\u91cf\u6307\u6570\u7ea7\u589e\u957f\uff0c\u80fd\u529b\u4e5f\u786e\u5b9e\u53d1\u751f\u4e86\u8d28\u7684\u98de\u8dc3\u3002\u8fd9\u8ba9\u5f88\u591a\u4eba\u76f8\u4fe1\uff0c\u8fd9\u6761\u8def\u80fd\u4e00\u76f4\u8d70\u4e0b\u53bb\u3002<\/p>\n\n\n\n

\u7136\u800c\uff0c\u5f53\u524d AI \u5708\u7684\u4e00\u4e2a\u70ed\u70b9\u4e89\u8bba\u5c31\u662f\uff1a\u6211\u4eec\u662f\u4e0d\u662f\u5feb\u8981\u649e\u4e0a\u300cScaling Laws\u300d\u7684\u5929\u82b1\u677f\u4e86\uff1f<\/p>\n\n\n\n

DeepMind \u7684\u6700\u65b0\u7814\u7a76\u4e3a\u8fd9\u4e2a\u4e89\u8bba\u63d0\u4f9b\u4e86\u65b0\u7684\u8bc1\u636e\u3002\u4ed6\u4eec\u5c06\u51e0\u4f55\u4ee3\u6570\u4e0e\u901a\u4fe1\u590d\u6742\u5ea6\u7406\u8bba\u7ed3\u5408\uff0c\u8bc1\u660e\u4e86\u5411\u91cf\u5d4c\u5165\u7684\u80fd\u529b\u5b58\u5728\u4e00\u4e2a\u6570\u5b66\u4e0b\u754c\uff1a\u5bf9\u4e8e\u4efb\u610f\u7ed9\u5b9a\u7684\u5d4c\u5165\u7ef4\u5ea6 d\uff0c\u5f53\u6587\u6863\u6570\u91cf\u8d85\u8fc7\u67d0\u4e2a\u4e34\u754c\u70b9\u65f6\uff0c\u603b\u4f1a\u5b58\u5728\u4e00\u4e9b\u76f8\u5173\u6587\u6863\u7ec4\u5408\u662f\u65e0\u8bba\u5982\u4f55\u90fd\u65e0\u6cd5\u901a\u8fc7\u67e5\u8be2\u540c\u65f6\u53ec\u56de\u7684\u3002<\/strong><\/p>\n\n\n\n

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\"\"<\/figure>\n\n\n\n
    \n
  • \u8bba\u6587\u6807\u9898\uff1aOn the Theoretical Limitations of Embedding-Based Retrieval<\/li>\n\n\n\n
  • arXiv \u5730\u5740\uff1ahttps:\/\/arxiv.org\/pdf\/2508.21038<\/li>\n<\/ul>\n\n\n\n

    \u8fd9\u4e00\u7406\u8bba\u74f6\u9888\u5728\u73b0\u5b9e\u4e2d\u6700\u76f4\u63a5\u7684\u4f53\u73b0\uff0c\u5c31\u662f\u68c0\u7d22\u589e\u5f3a\u751f\u6210\uff08RAG\uff09\u3002RAG \u7684\u5de5\u4f5c\u673a\u5236\u662f\uff1a\u5148\u7528\u5411\u91cf\u5d4c\u5165\u4ece\u77e5\u8bc6\u5e93\u4e2d\u68c0\u7d22\u76f8\u5173\u4fe1\u606f\uff0c\u518d\u4ea4\u7ed9\u5927\u6a21\u578b\u751f\u6210\u7b54\u6848\u3002\u4f46 DeepMind \u7684\u8bc1\u660e\u8868\u660e\uff0c\u5f53\u77e5\u8bc6\u5e93\u89c4\u6a21\u8db3\u591f\u5927\u3001\u4e00\u4e2a\u95ee\u9898\u9700\u8981\u591a\u4efd\u6587\u6863\u5171\u540c\u56de\u7b54\u65f6\uff0c\u5373\u4f7f\u91c7\u7528\u6700\u5148\u8fdb\u7684\u5d4c\u5165\u6a21\u578b\uff0c\u4e5f\u53ef\u80fd\u56e0\u4e3a\u7ef4\u5ea6\u4e0d\u8db3\u800c\u65e0\u6cd5\u5b8c\u6574\u53ec\u56de\u5173\u952e\u4fe1\u606f\u3002\u8fd9\u4f1a\u5bfc\u81f4\u5927\u6a21\u578b\u5728\u751f\u6210\u65f6\u53d7\u5230\u9519\u8bef\u6216\u4e0d\u5b8c\u6574\u4e0a\u4e0b\u6587\u7684\u5e72\u6270\u3002<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

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    \u8bba\u6587\u4e00\u4f5c\u3001\u7ea6\u7ff0\u970d\u666e\u91d1\u65af\u5927\u5b66\u535a\u58eb\u751f Orion Weller \u8868\u793a\uff0c\u5728\u5f53\u524d\u7684\u4fe1\u606f\u68c0\u7d22\u9886\u57df\uff0c\u5927\u5bb6\u5e0c\u671b\u5d4c\u5165\u6a21\u578b\u80fd\u591f\u540c\u65f6\u627f\u62c5\u6307\u4ee4\u7406\u89e3\u548c\u63a8\u7406\u7b49\u4e00\u5207\u80fd\u529b\u3002\u9057\u61be\u7684\u662f\uff0c\u5355\u5411\u91cf\u5d4c\u5165\u6a21\u578b\u5728\u7406\u8bba\u548c\u5b9e\u8bc1\u4e0a\u90fd\u505a\u4e0d\u5230\uff0c\u5b83\u4eec\u5b58\u5728\u6839\u672c\u6027\u9650\u5236\u3002\u901a\u8fc7\u5f00\u6e90\u7684\u7b80\u5355\u8bc4\u6d4b\u4efb\u52a1\uff0c\u4ed6\u4eec\u8bc1\u5b9e\u4e86\u5373\u4fbf\u662f SOTA \u6a21\u578b\u4e5f\u8868\u73b0\u7cdf\u7cd5\u3002<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    \u603b\u7684\u6765\u8bf4\uff0c\u8fd9\u7bc7\u5de5\u4f5c\u7684\u8d21\u732e\u53ef\u4ee5\u603b\u7ed3\u4e3a\u4ee5\u4e0b\u4e09\u70b9\uff1a<\/p>\n\n\n\n

      \n
    • \u4e3a\u5d4c\u5165\u6a21\u578b\u7684\u6839\u672c\u6027\u5c40\u9650\u63d0\u4f9b\u4e86\u7406\u8bba\u57fa\u7840\uff1b<\/li>\n\n\n\n
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    • \u6784\u5efa\u4e86\u4e00\u4e2a\u540d\u4e3a LIMIT \u7684\u7b80\u5355\u3001\u771f\u5b9e\u7684\u81ea\u7136\u8bed\u8a00\u5b9e\u4f8b\uff0c\u5373\u4f7f\u662f\u6700\u5148\u8fdb\u7684\u5d4c\u5165\u6a21\u578b\u4e5f\u65e0\u6cd5\u89e3\u51b3\u3002<\/li>\n<\/ul>\n\n\n\n

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      \"\"<\/figure>\n\n\n\n

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      \"\"<\/figure>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

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        \n
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        \u5b9e\u8bc1\u8054\u7cfb<\/strong><\/p>\n\n\n\n

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        \"\"<\/figure>\n\n\n\n

        \u5b9e\u8bc1\u8054\u7cfb<\/strong><\/p>\n\n\n\n

        \u771f\u5b9e\u4e16\u754c\u6570\u636e\u96c6<\/strong><\/p>\n\n\n\n

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        \"\"<\/figure>\n\n\n\n

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        \"\"<\/figure>\n\n\n\n
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