{"id":1354,"date":"2024-05-05T21:31:11","date_gmt":"2024-05-05T21:31:11","guid":{"rendered":"https:\/\/www.nicekj.com\/?p=1354"},"modified":"2024-05-05T21:31:11","modified_gmt":"2024-05-05T21:31:11","slug":"jiechu-gpt-zhishixianzhidazaowuxianduihuajiqirenembeddings-yuanlijichu","status":"publish","type":"post","link":"https:\/\/www.nicekj.com\/jiechu-gpt-zhishixianzhidazaowuxianduihuajiqirenembeddings-yuanlijichu.html","title":{"rendered":"\u89e3\u9664 GPT \u77e5\u8bc6\u9650\u5236\u6253\u9020\u65e0\u9650\u5bf9\u8bdd\u673a\u5668\u4eba\uff1aEmbeddings \u539f\u7406\u57fa\u7840"},"content":{"rendered":"<p>LLMs\uff08\u8bed\u8a00\u6a21\u578b\uff09\u53ea\u80fd\u5229\u7528\u8bad\u7ec3\u6240\u7528\u6570\u636e\u96c6\u6765\u56de\u7b54\u95ee\u9898\uff0c\u8fd9\u4e5f\u610f\u5473\u7740\u5bf9\u4e8e ChatGPT \u800c\u8a00\uff0c\u5b83\u53ea\u80fd\u56de\u7b54\u5173\u4e8e 2021 \u5e74 9 \u6708\u4e4b\u524d\u7684\u4fe1\u606f\u3002\u800c\u5229\u7528 Embedding \u4f60\u53ef\u4ee5\u505a\u5230\u8ba9 GPT \u56de\u7b54\uff1a<\/p>\n<ul>\n<li>2021 \u5e74 9 \u6708\u4ee5\u540e\u7684\u65b0\u95fb<\/li>\n<li>\u516c\u53f8\u5185\u90e8\u7684\u975e\u516c\u5f00\u6587\u6863\uff08\u5982\u679c\u4f60\u4fe1\u4efb OpenAI \u7684\u5546\u7528\u670d\u52a1\uff09<\/li>\n<li>\u8fc7\u5f80\u5bf9\u8bdd\u8bb0\u5f55\uff08\u4ee5\u5b9e\u73b0\u65e0\u9650 token \u7684\u5bf9\u8bdd\u673a\u5668\u4eba\uff09<\/li>\n<\/ul>\n<p>\u672c\u6587\u5c06\u901a\u8fc7\u4f7f\u7528 Embedding \u641c\u7d22\u6765\u8ba9 GPT \u80fd\u591f\u56de\u7b54\u4e0e\u676d\u5dde\u4e9a\u8fd0\u4f1a\u76f8\u5173\u7684\u95ee\u9898\uff0c\u5e76\u6700\u7ec8\u5b9e\u73b0\u80fd\u65e0\u89c6 token \u9650\u5236\u65e0\u9650\u5bf9\u8bdd\u7684 AI \u52a9\u624b\u3002<\/p>\n<h2 data-id=\"heading-0\">\u63d0\u4f9b\u4e0a\u4e0b\u6587<\/h2>\n<p>LLMs \u6709\u4e24\u79cd\u9014\u5f84\u5b66\u4e60\u77e5\u8bc6\uff1a<\/p>\n<ul>\n<li>\u901a\u8fc7\u6a21\u578b\u6743\u91cd\u5b66\u4e60\uff08\u6bd4\u5982\u901a\u8fc7\u8bad\u7ec3\u96c6\u5bf9\u6a21\u578b\u8fdb\u884c Fine-tuning\uff09<\/li>\n<li>\u901a\u8fc7\u6a21\u578b\u8f93\u5165\uff08\u6bd4\u5982\u63d2\u5165\u4e0a\u4e0b\u6587\u4fe1\u606f\u5230\u8f93\u5165\u6d88\u606f\u4e2d\uff09<\/li>\n<\/ul>\n<p>OpenAI \u63a8\u8350\u5728\u4fe1\u606f\u4e2d\u63d2\u5165\u4e0a\u4e0b\u6587\u4fe1\u606f\u4f5c\u4e3a\u4e3a\u6a21\u578b\u8f93\u5165\u65b0\u77e5\u8bc6\u7684\u65b9\u5f0f\uff0c\u800c\u4e0d\u662f\u4f7f\u7528 Fine-tuning\u3002\u8fd9\u6216\u8bb8\u6709\u51fa\u4e8e\u6210\u672c\u7684\u8003\u8651\uff0c\u4f46\u66f4\u91cd\u8981\u7684\u662f Fine-tuning \u65e0\u6cd5\u4fdd\u8bc1\u4f5c\u7528\u4e8e\u53c2\u6570\u6743\u91cd\u7684\u8bad\u7ec3\u7ed3\u679c\u80fd\u4f53\u73b0\u5230\u5177\u4f53\u7684\u6bcf\u4e00\u6b21\u5bf9\u8bdd\u4e0a\u3002\u5373 Fine-tuning \u4e0d\u592a\u9002\u5408\u7528\u6765\u8ba9 LLMs \u56de\u5fc6\u67d0\u4e00\u4e2a\u5177\u4f53\u4e8b\u4f8b\uff0c\u800c\u66f4\u591a\u7528\u4e8e\u8ba9\u5176\u9002\u5e94\u7279\u5b9a\u5f62\u5f0f\u548c\u98ce\u683c\u7684\u4efb\u52a1\u3002<\/p>\n<p>\u6253\u4e2a\u6bd4\u65b9\uff1aFine-tuning \u8c03\u6574\u6a21\u578b\u6743\u91cd\u7684\u8fc7\u7a0b\u5c31\u50cf\u662f\u8ba9\u80fd\u5904\u7406\u5404\u79cd\u4e0d\u540c\u901a\u7528\u4efb\u52a1\u7684\u5927\u5b66\u751f\u63a5\u53d7\u7279\u5b9a\u5c97\u4f4d\u7684\u57f9\u8bad\u3002\u800c\u63d2\u5165\u641c\u7d22\u7684\u7ed3\u679c\u5219\u662f\u8ba9\u5728\u5904\u7406\u5177\u4f53\u4efb\u52a1\u65f6\u7ed9\u51fa\u9488\u5bf9\u6027\u7684\u51e0\u6761\u53c2\u8003\u4fe1\u606f\uff0c\u80fd\u8ba9\u4eba\u5f88\u5bb9\u6613\u5e94\u7528\u5230\u5f53\u524d\u7684\u4e8b\u4f8b\u4e2d\u3002<\/p>\n<p>\u641c\u7d22\u76f8\u6bd4\u4e8e Fine-tuning \u6709\u4e00\u4e2a\u7f3a\u70b9\u662f\uff1a\u63d2\u5165\u7684\u4fe1\u606f\u4f5c\u4e3a\u5bf9\u8bdd\u7684\u4e00\u90e8\u5206\uff0c\u4e5f\u4f1a\u53d7\u9650\u4e8e\u6a21\u578b\u7684\u5bf9\u8bdd\u6700\u5927\u6587\u672c\u91cf\u9650\u5236\u3002<\/p>\n<table>\n<thead>\n<tr>\n<th>\u6a21\u578b<\/th>\n<th>\u6700\u5927\u6587\u672c\u91cf<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>gpt-3.5-turbo<\/td>\n<td>4,096 tokens<\/td>\n<\/tr>\n<tr>\n<td>gpt-4<\/td>\n<td>8,192 tokens<\/td>\n<\/tr>\n<tr>\n<td>gpt-4-32k<\/td>\n<td>32,768 tokens<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u4e5f\u5c31\u662f\u8bf4\u6211\u4eec\u505a\u4e0d\u5230\u628a\u6574\u672c\u7ea2\u697c\u68a6\u539f\u8457\u63d2\u5165\u5bf9\u8bdd\u5f53\u4e2d\uff0c\u5e76\u5411 GPT \u63d0\u95ee\u4e66\u4e2d\u5185\u5bb9\uff0c\u56e0\u4e3a GPT \u7684\u5bf9\u8bdd token \u5bb9\u91cf\u6839\u672c\u65e0\u6cd5\u652f\u6301\u8f93\u5165\u8fd9\u4e48\u591a\u4fe1\u606f\u3002<\/p>\n<p>\u56e0\u6b64\u6211\u4eec\u5e94\u8be5\u5411 GPT \u63d0\u4f9b\u5c3d\u53ef\u80fd\u76f8\u5173\u7684\u90e8\u5206\u6709\u9650\u4fe1\u606f\u3002<\/p>\n<h2 data-id=\"heading-1\">\u5d4c\u5165\u641c\u7d22<\/h2>\n<p>\u6211\u4eec\u53ef\u4ee5\u5728\u5e9e\u5927\u7684\u6570\u636e\u96c6\u4e2d\u641c\u7d22\u6700\u76f8\u5173\u7684\u5185\u5bb9\u6765\u4f5c\u4e3a\u5bf9\u8bdd\u7684\u4e0a\u4e0b\u6587\uff0c\u8fd9\u91cc\u6709\u51e0\u79cd\u4e0d\u540c\u7684\u65b9\u6cd5\uff1a<\/p>\n<ul>\n<li>\u5173\u952e\u8bcd\u641c\u7d22<\/li>\n<li>\u56fe\u641c\u7d22<\/li>\n<li>\u5d4c\u5165\u641c\u7d22<\/li>\n<\/ul>\n<p>\u5728\u4e3a LLMs \u63d0\u4f9b\u5bf9\u8bdd\u4e0a\u4e0b\u6587\u4fe1\u606f\u65f6\u5e38\u7528\u5230\u7684\u5c31\u662f\u6700\u540e\u4e00\u79cd\u5d4c\u5165\u641c\u7d22\uff0c\u4e5f\u5c31\u662f\u6211\u4eec\u5e38\u8bf4\u7684 Embeddings\u3002\u5b83\u76f8\u6bd4\u4e8e\u4ec5\u4f9d\u8d56\u5173\u952e\u8bcd\u7684\u641c\u7d22\u7684\u4f18\u52bf\u6709\u5f88\u591a\u3002<\/p>\n<p>\u6bd4\u5982\u5d4c\u5165\u641c\u7d22\u53ef\u4ee5\u5904\u7406\u4e0d\u540c\u8bed\u8a00\u7684\u5185\u5bb9\uff0c\u4e0d\u540c\u8bed\u8a00\u8868\u8fbe\u540c\u4e00\u79cd\u610f\u601d\u7684\u6587\u672c\u5728\u5d4c\u5165\u7a7a\u95f4\u7684\u8bed\u4e49\u7ef4\u5ea6\u4e0a\u66f4\u63a5\u8fd1\u3002\u6bd4\u5982\u641c\u7d22 \u201cI like to eat apples.\u201d \u5c31\u66f4\u5bb9\u6613\u5339\u914d\u5230 \u201c\u6211\u559c\u6b22\u5403\u82f9\u679c\u201d \u800c\u4e0d\u662f\u540c\u6837\u51fa\u73b0 &#8220;Apple&#8221; \u5355\u8bcd\u4e14\u540c\u4e3a\u82f1\u6587\u7684\u53e5\u5b50 \u201cI bought some stocks of Apple Inc.\u201d\u3002<\/p>\n<pre><\/div><div class=\"code-block-extension-headerRight\"><span class=\"code-block-extension-lang\">vbnet<\/span><div class=\"code-block-extension-copyCodeBtn\">\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div><code class=\"hljs language-vbnet code-block-extension-codeShowNum\" lang=\"vbnet\"><span class=\"code-block-extension-codeLine\" data-line-num=\"1\"><span class=\"hljs-symbol\">Query:<\/span> I <span class=\"hljs-built_in\">like<\/span> <span class=\"hljs-keyword\">to<\/span> eat apples.<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"2\">[<span class=\"hljs-number\">1.00000<\/span>] I <span class=\"hljs-built_in\">like<\/span> <span class=\"hljs-keyword\">to<\/span> eat apples.<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"3\">[<span class=\"hljs-number\">0.87726<\/span>] \u6211\u559c\u6b22\u5403\u82f9\u679c<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"4\">[<span class=\"hljs-number\">0.82987<\/span>] I bought some stocks <span class=\"hljs-keyword\">of<\/span> Apple Inc.<\/span>\n<\/code><\/pre>\n<p>\u8fd0\u7528 Embeddings \u8fd8\u53ef\u4ee5\u5904\u7406\u4e00\u4e9b\u5e76\u4e0d\u80fd\u76f4\u63a5\u5339\u914d\u6587\u672c\u7684\u60c5\u51b5\uff0c\u6bd4\u5982\u4e0b\u9762\u67e5\u8be2\u7684\u8bed\u53e5\u4e2d\u5e76\u6ca1\u6709\u76f4\u63a5\u51fa\u73b0\u82f9\u679c\u516c\u53f8\u80a1\u7968\u76f8\u5173\u5b57\u773c\uff0c\u4f46\u7531\u4e8e\u903b\u8f91\u4e0a\u7684\u5173\u8054\u6027\u800c\u88ab\u8ba4\u4e3a\u4e0e\u5176\u9ad8\u5ea6\u76f8\u5173\u3002<\/p>\n<pre><\/div><div class=\"code-block-extension-headerRight\"><span class=\"code-block-extension-lang\">vbnet<\/span><div class=\"code-block-extension-copyCodeBtn\">\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div><code class=\"hljs language-vbnet code-block-extension-codeShowNum\" lang=\"vbnet\"><span class=\"code-block-extension-codeLine\" data-line-num=\"1\"><span class=\"hljs-symbol\">Query:<\/span> I tend <span class=\"hljs-keyword\">to<\/span> favor value investing <span class=\"hljs-built_in\">and<\/span> usually invest <span class=\"hljs-keyword\">in<\/span> some tech giants.<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"2\">[<span class=\"hljs-number\">0.84487<\/span>] I bought some stocks <span class=\"hljs-keyword\">of<\/span> Apple Inc.<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"3\">[<span class=\"hljs-number\">0.78884<\/span>] I <span class=\"hljs-built_in\">like<\/span> <span class=\"hljs-keyword\">to<\/span> eat apples.<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"4\">[<span class=\"hljs-number\">0.72575<\/span>] \u6211\u559c\u6b22\u5403\u82f9\u679c<\/span>\n<\/code><\/pre>\n<p>\u9700\u8981\u6ce8\u610f\u7684\u662f\u4e0d\u80fd\u5c06 \u201c\u5d4c\u5165\u201d \u7406\u89e3\u6210 \u201c\u5c06\u4e0a\u4e0b\u6587\u4fe1\u606f<strong>\u5d4c\u5165<\/strong>\u5230\u5bf9\u8bdd\u5f53\u4e2d\u201d\uff0c\u8fd9\u91cc\u7684 \u201c\u5d4c\u5165\u201d \u5e94\u8be5\u6307\u7684\u662f\uff1a\u5c06\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u63cf\u8ff0\u7684\u95ee\u9898\u6587\u672c\uff0c<strong>\u5d4c\u5165<\/strong>\u5230\u9ad8\u7ef4\u5ea6\u7684\u5411\u91cf\u7a7a\u95f4\u4e2d\uff0c\u4ee5\u5b9e\u73b0\u7a0b\u5f0f\u5316\u7684\u5206\u6790\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u89e3\u91ca Embeddings \u7684\u6982\u5ff5\u3002<\/p>\n<h2 data-id=\"heading-2\">\u5d4c\u5165\u7684\u6982\u5ff5<\/h2>\n<p>\u5f53\u6211\u4eec\u8c08\u5230 \u201c\u5d4c\u5165\uff08Embedding\uff09\u201d \u65f6\uff0c\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u5b66\u9886\u57df\u7684\u4f8b\u5b50\u662f\u5c06\u4e00\u4e2a\u5411\u91cf\u5d4c\u5165\u5230\u4e00\u4e2a\u66f4\u9ad8\u7ef4\u7684\u7a7a\u95f4\u4e2d\u3002<\/p>\n<p>\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u4e8c\u7ef4\u5e73\u9762\u4e0a\u7684\u70b9A\uff0c\u5b83\u7684\u5750\u6807\u662f <code>(2, 3)<\/code>\u3002\u6211\u4eec\u60f3\u8981\u5c06\u8fd9\u4e2a\u70b9\u5d4c\u5165\u5230\u4e00\u4e2a\u4e09\u7ef4\u7a7a\u95f4\u4e2d\uff0c\u53ef\u4ee5\u5c06\u5176\u8868\u793a\u4e3a <code>(2, 3, 0)<\/code>\u3002\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u5728\u4e09\u7ef4\u7a7a\u95f4\u4e2d\u5d4c\u5165\u4e86\u4e8c\u7ef4\u70b9 A\u3002<\/p>\n<p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u539f\u59cb\u7684\u4e8c\u7ef4\u70b9 A \u88ab\u5d4c\u5165\u5230\u4e00\u4e2a\u66f4\u9ad8\u7ef4\u7684\u7a7a\u95f4\u4e2d\uff0c\u4f7f\u5f97\u6211\u4eec\u53ef\u4ee5\u5728\u65b0\u7684\u7a7a\u95f4\u4e2d\u5229\u7528\u989d\u5916\u7684\u7ef4\u5ea6\u6765\u8868\u793a\u66f4\u591a\u7684\u4fe1\u606f\u3002\u8fd9\u79cd\u5d4c\u5165\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5904\u7406\u6570\u636e\u3002\u7c7b\u4f3c\u5730\uff0c\u5d4c\u5165\u53ef\u4ee5\u5e94\u7528\u4e8e\u5176\u4ed6\u9886\u57df\uff0c\u5982\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e2d\u7684\u8bcd\u5d4c\u5165\uff0c\u5c06\u5355\u8bcd\u5d4c\u5165\u5230\u4e00\u4e2a\u9ad8\u7ef4\u5411\u91cf\u7a7a\u95f4\u4e2d\uff0c\u4ee5\u4fbf\u8fdb\u884c\u8bed\u4e49\u5206\u6790\u548c\u6587\u672c\u5904\u7406\u3002<\/p>\n<p>\u5728\u8bcd\u5d4c\u5165\u4e2d\uff0c\u5411\u91cf\u7a7a\u95f4\u7684\u51e0\u4f55\u610f\u4e49\u53ef\u4ee5\u63d0\u4f9b\u5bf9\u8bcd\u6c47\u5173\u7cfb\u7684\u4e00\u4e9b\u76f4\u89c2\u7406\u89e3\u3002<\/p>\n<p>\u5411\u91cf\u7a7a\u95f4\u4e2d\u7684\u8ddd\u79bb\u53ef\u4ee5\u8868\u793a\u5355\u8bcd\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u3002\u5982\u679c\u4e24\u4e2a\u5355\u8bcd\u7684\u5411\u91cf\u5728\u7a7a\u95f4\u4e2d\u8ddd\u79bb\u8f83\u8fd1\uff0c\u90a3\u4e48\u5b83\u4eec\u5728\u8bed\u4e49\u4e0a\u53ef\u80fd\u5177\u6709\u8f83\u9ad8\u7684\u76f8\u4f3c\u6027\u3002\u4f8b\u5982\uff0c\u5728\u8bad\u7ec3\u826f\u597d\u7684\u8bcd\u5d4c\u5165\u6a21\u578b\u4e2d\uff0c&#8221;king&#8221; \u548c &#8220;queen&#8221; \u8fd9\u4e24\u4e2a\u5355\u8bcd\u7684\u5411\u91cf\u5728\u7a7a\u95f4\u4e2d\u7684\u8ddd\u79bb\u53ef\u80fd\u6bd4\u8f83\u63a5\u8fd1\uff0c\u56e0\u4e3a\u5b83\u4eec\u5728\u8bed\u4e49\u4e0a\u5177\u6709\u76f8\u4f3c\u7684\u542b\u4e49\u3002<\/p>\n<p>\u5411\u91cf\u7a7a\u95f4\u4e2d\u7684\u65b9\u5411\u53ef\u4ee5\u8868\u793a\u8bed\u4e49\u5173\u7cfb\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u4e24\u4e2a\u5355\u8bcd\u5411\u91cf\u7684\u5dee\u5f02\u5411\u91cf\u6765\u6355\u6349\u5b83\u4eec\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4f8b\u5982\uff0c\u4f7f\u7528 &#8220;king&#8221; \u5411\u91cf\u51cf\u53bb &#8220;man&#8221; \u5411\u91cf\uff0c\u518d\u52a0\u4e0a &#8220;woman&#8221; \u5411\u91cf\uff0c\u53ef\u80fd\u4f1a\u5f97\u5230\u4e00\u4e2a\u63a5\u8fd1\u4e8e &#8220;queen&#8221; \u5411\u91cf\u7684\u7ed3\u679c\u3002\u8fd9\u8bf4\u660e\u4e86\u5728\u5411\u91cf\u7a7a\u95f4\u4e2d\uff0c&#8221;king&#8221; \u548c &#8220;queen&#8221; \u4e4b\u95f4\u7684\u5173\u7cfb\u53ef\u80fd\u4e0e &#8220;man&#8221; \u548c &#8220;woman&#8221; \u4e4b\u95f4\u7684\u5173\u7cfb\u76f8\u4f3c\u3002<\/p>\n<p>\u5411\u91cf\u7a7a\u95f4\u4e2d\u7684\u7ebf\u6027\u7ec4\u5408\u4e5f\u53ef\u4ee5\u8868\u793a\u8bed\u4e49\u5173\u7cfb\u3002\u901a\u8fc7\u5c06\u4e24\u4e2a\u5355\u8bcd\u5411\u91cf\u76f8\u52a0\u6216\u76f8\u51cf\uff0c\u6211\u4eec\u53ef\u4ee5\u751f\u6210\u65b0\u7684\u5411\u91cf\uff0c\u8868\u793a\u4e24\u4e2a\u5355\u8bcd\u4e4b\u95f4\u7684\u5408\u6210\u6216\u5bf9\u6bd4\u5173\u7cfb\u3002\u4f8b\u5982\uff0c\u4f7f\u7528 &#8220;Paris&#8221; \u5411\u91cf\u51cf\u53bb &#8220;France&#8221; \u5411\u91cf\uff0c\u518d\u52a0\u4e0a &#8220;Italy&#8221; \u5411\u91cf\uff0c\u53ef\u80fd\u4f1a\u5f97\u5230\u4e00\u4e2a\u63a5\u8fd1\u4e8e &#8220;Rome&#8221; \u5411\u91cf\u7684\u7ed3\u679c\u3002<\/p>\n<p><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mover accent=\"true\"><mrow><mi>k<\/mi><mi>i<\/mi><mi>n<\/mi><mi>g<\/mi><\/mrow><mo>\u20d7<\/mo><\/mover><mo>\u2212<\/mo><mover accent=\"true\"><mrow><mi>m<\/mi><mi>a<\/mi><mi>n<\/mi><\/mrow><mo>\u20d7<\/mo><\/mover><mo>+<\/mo><mover accent=\"true\"><mrow><mi>w<\/mi><mi>o<\/mi><mi>m<\/mi><mi>a<\/mi><mi>n<\/mi><\/mrow><mo>\u20d7<\/mo><\/mover><mo>\u2248<\/mo><mover accent=\"true\"><mrow><mi>q<\/mi><mi>u<\/mi><mi>e<\/mi><mi>e<\/mi><mi>n<\/mi><\/mrow><mo>\u20d7<\/mo><\/mover><\/mrow><annotation encoding=\"application\/x-tex\">\\vec{king} &#8211; \\vec{man} + \\vec{woman} \\approx \\vec{queen}<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height:1.1719em;vertical-align:-0.1944em;\"><\/span><span class=\"mord accent\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height:0.9774em;\"><span style=\"top:-3em;\"><span class=\"pstrut\" style=\"height:3em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">kin<\/span><span class=\"mord mathnormal\" style=\"margin-right:0.03588em;\">g<\/span><\/span><\/span><span style=\"top:-3.2634em;\"><span class=\"pstrut\" style=\"height:3em;\"><\/span><span class=\"accent-body\" style=\"left:-0.2355em;\"><span class=\"overlay\" style=\"height:0.714em;width:0.471em;\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"0.471em\" height=\"0.714em\" style=\"width:0.471em\" viewBox=\"0 0 471 714\" preserveAspectRatio=\"xMinYMin\"><path d=\"M377 20c0-5.333 1.833-10 5.5-14S391 0 397 0c4.667 0 8.667 1.667 12 5\n3.333 2.667 6.667 9 10 19 6.667 24.667 20.333 43.667 41 57 7.333 4.667 11\n10.667 11 18 0 6-1 10-3 12s-6.667 5-14 9c-28.667 14.667-53.667 35.667-75 63\n-1.333 1.333-3.167 3.5-5.5 6.5s-4 4.833-5 5.5c-1 .667-2.5 1.333-4.5 2s-4.333 1\n-7 1c-4.667 0-9.167-1.833-13.5-5.5S337 184 337 178c0-12.667 15.667-32.333 47-59\nH213l-171-1c-8.667-6-13-12.333-13-19 0-4.667 4.333-11.333 13-20h359\nc-16-25.333-24-45-24-59z\"><\/path><\/svg><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height:0.1944em;\"><span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right:0.2222em;\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right:0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height:0.7973em;vertical-align:-0.0833em;\"><\/span><span class=\"mord accent\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height:0.714em;\"><span style=\"top:-3em;\"><span class=\"pstrut\" style=\"height:3em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">man<\/span><\/span><\/span><span style=\"top:-3em;\"><span class=\"pstrut\" style=\"height:3em;\"><\/span><span class=\"accent-body\" style=\"left:-0.2355em;\"><span class=\"overlay\" style=\"height:0.714em;width:0.471em;\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"0.471em\" height=\"0.714em\" style=\"width:0.471em\" viewBox=\"0 0 471 714\" preserveAspectRatio=\"xMinYMin\"><path d=\"M377 20c0-5.333 1.833-10 5.5-14S391 0 397 0c4.667 0 8.667 1.667 12 5\n3.333 2.667 6.667 9 10 19 6.667 24.667 20.333 43.667 41 57 7.333 4.667 11\n10.667 11 18 0 6-1 10-3 12s-6.667 5-14 9c-28.667 14.667-53.667 35.667-75 63\n-1.333 1.333-3.167 3.5-5.5 6.5s-4 4.833-5 5.5c-1 .667-2.5 1.333-4.5 2s-4.333 1\n-7 1c-4.667 0-9.167-1.833-13.5-5.5S337 184 337 178c0-12.667 15.667-32.333 47-59\nH213l-171-1c-8.667-6-13-12.333-13-19 0-4.667 4.333-11.333 13-20h359\nc-16-25.333-24-45-24-59z\"><\/path><\/svg><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right:0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right:0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height:0.714em;\"><\/span><span class=\"mord accent\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height:0.714em;\"><span style=\"top:-3em;\"><span class=\"pstrut\" style=\"height:3em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right:0.02691em;\">w<\/span><span class=\"mord mathnormal\">o<\/span><span class=\"mord mathnormal\">man<\/span><\/span><\/span><span style=\"top:-3em;\"><span class=\"pstrut\" style=\"height:3em;\"><\/span><span class=\"accent-body\" style=\"left:-0.2355em;\"><span class=\"overlay\" style=\"height:0.714em;width:0.471em;\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"0.471em\" height=\"0.714em\" style=\"width:0.471em\" viewBox=\"0 0 471 714\" preserveAspectRatio=\"xMinYMin\"><path d=\"M377 20c0-5.333 1.833-10 5.5-14S391 0 397 0c4.667 0 8.667 1.667 12 5\n3.333 2.667 6.667 9 10 19 6.667 24.667 20.333 43.667 41 57 7.333 4.667 11\n10.667 11 18 0 6-1 10-3 12s-6.667 5-14 9c-28.667 14.667-53.667 35.667-75 63\n-1.333 1.333-3.167 3.5-5.5 6.5s-4 4.833-5 5.5c-1 .667-2.5 1.333-4.5 2s-4.333 1\n-7 1c-4.667 0-9.167-1.833-13.5-5.5S337 184 337 178c0-12.667 15.667-32.333 47-59\nH213l-171-1c-8.667-6-13-12.333-13-19 0-4.667 4.333-11.333 13-20h359\nc-16-25.333-24-45-24-59z\"><\/path><\/svg><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right:0.2778em;\"><\/span><span class=\"mrel\">\u2248<\/span><span class=\"mspace\" style=\"margin-right:0.2778em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height:0.9084em;vertical-align:-0.1944em;\"><\/span><span class=\"mord accent\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height:0.714em;\"><span style=\"top:-3em;\"><span class=\"pstrut\" style=\"height:3em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right:0.03588em;\">q<\/span><span class=\"mord mathnormal\">u<\/span><span class=\"mord mathnormal\">ee<\/span><span class=\"mord mathnormal\">n<\/span><\/span><\/span><span style=\"top:-3em;\"><span class=\"pstrut\" style=\"height:3em;\"><\/span><span class=\"accent-body\" style=\"left:-0.2355em;\"><span class=\"overlay\" style=\"height:0.714em;width:0.471em;\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"0.471em\" height=\"0.714em\" style=\"width:0.471em\" viewBox=\"0 0 471 714\" preserveAspectRatio=\"xMinYMin\"><path d=\"M377 20c0-5.333 1.833-10 5.5-14S391 0 397 0c4.667 0 8.667 1.667 12 5\n3.333 2.667 6.667 9 10 19 6.667 24.667 20.333 43.667 41 57 7.333 4.667 11\n10.667 11 18 0 6-1 10-3 12s-6.667 5-14 9c-28.667 14.667-53.667 35.667-75 63\n-1.333 1.333-3.167 3.5-5.5 6.5s-4 4.833-5 5.5c-1 .667-2.5 1.333-4.5 2s-4.333 1\n-7 1c-4.667 0-9.167-1.833-13.5-5.5S337 184 337 178c0-12.667 15.667-32.333 47-59\nH213l-171-1c-8.667-6-13-12.333-13-19 0-4.667 4.333-11.333 13-20h359\nc-16-25.333-24-45-24-59z\"><\/path><\/svg><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height:0.1944em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<h2 data-id=\"heading-3\">Embedding API<\/h2>\n<p>OpenAI \u63d0\u4f9b\u4e86 <code>text-embedding-ada-002<\/code> \u6a21\u578b\u7528\u6765\u5b9e\u73b0 Embedding\uff0c\u4e5f\u5c31\u662f\u8bf4\u5c06\u6587\u672c\u8f6c\u6362\u6210\u5411\u91cf\uff1a<\/p>\n<pre><\/div><div class=\"code-block-extension-headerRight\"><span class=\"code-block-extension-lang\">typescript<\/span><div class=\"code-block-extension-copyCodeBtn\">\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div><code class=\"hljs language-typescript code-block-extension-codeShowNum\" lang=\"typescript\"><span class=\"code-block-extension-codeLine\" data-line-num=\"1\"><span class=\"hljs-keyword\">import<\/span> { <span class=\"hljs-title class_\">OpenAI<\/span> } <span class=\"hljs-keyword\">from<\/span> <span class=\"hljs-string\">'openai'<\/span>;<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"2\"><\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"3\"><span class=\"hljs-keyword\">const<\/span> api = <span class=\"hljs-keyword\">new<\/span> <span class=\"hljs-title class_\">OpenAI<\/span>({});<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"4\"><\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"5\"><span class=\"hljs-keyword\">const<\/span> response = <span class=\"hljs-keyword\">await<\/span> api.<span class=\"hljs-property\">embeddings<\/span>.<span class=\"hljs-title function_\">create<\/span>({<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"6\">  <span class=\"hljs-attr\">model<\/span>: <span class=\"hljs-string\">'text-embedding-ada-002'<\/span>,<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"7\">  <span class=\"hljs-attr\">input<\/span>: <span class=\"hljs-string\">'\u6211\u559c\u6b22\u5403\u82f9\u679c'<\/span>,<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"8\">});<\/span>\n<\/code><\/pre>\n<p>\u8fd9\u91cc\u6253\u5370 <code>response.data[0].embedding<\/code> \u53ef\u4ee5\u5f97\u5230\u4e00\u4e2a\u5305\u542b 1536 \u4e2a\u6d6e\u70b9\u6570\u7684\u6570\u7ec4\uff0c\u8fd9\u5c31\u662f\u6211\u4eec\u4e4b\u540e\u7528\u4e8e\u5206\u6790\u548c\u641c\u7d22\u7684 Embeddings \u5411\u91cf\u3002\u6ce8\u610f\u8fd9\u91cc\u5411\u91cf\u7684\u7ef4\u5ea6\u662f\u56fa\u5b9a\u7684\uff0c\u4efb\u610f\u957f\u5ea6\u7684\u6587\u672c\u8f93\u5165\u90fd\u4f1a\u5f97\u5230\u76f8\u540c\u957f\u5ea6\u7684\u6570\u7ec4\u3002<\/p>\n<h2 data-id=\"heading-4\">\u5411\u91cf\u67e5\u8be2<\/h2>\n<p>\u6839\u636e\u5411\u91cf\u67e5\u8be2\u76f8\u4f3c\u5ea6\u7684\u65b9\u6cd5\u975e\u5e38\u7b80\u5355\uff0c\u7c7b\u4f3c\u4f7f\u7528\u52fe\u80a1\u5b9a\u7406\u5c31\u53ef\u4ee5\u6c42\u89e3\u4e24\u4e2a\u4e8c\u7ef4\u5411\u91cf\u7684\u8ddd\u79bb\uff0c\u6211\u4eec\u53ef\u4ee5\u5f88\u8f7b\u677e\u5730\u6c42\u89e3\u4efb\u610f\u4e24\u4e2a\u5411\u91cf\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002\u8fd9\u91cc\u6211\u4f7f\u7528 ChatGPT \u751f\u6210\u4e86\u4e00\u6bb5\u4ee3\u7801\u5b9e\u73b0\uff1a<\/p>\n<blockquote>\n<p>Q: \u4f7f\u7528 TypeScript \u5b9e\u73b0\u4e00\u4e2a\u8ba1\u7b97\u4e24\u4e2a\u4efb\u610f\u7ef4\u5ea6\u5411\u91cf\u4e4b\u95f4\u8ddd\u79bb\u7684\u51fd\u6570<\/p>\n<\/blockquote>\n<blockquote>\n<p>A: \u4e0b\u9762\u662f\u4e00\u4e2a\u4f7f\u7528 TypeScript \u5b9e\u73b0\u8ba1\u7b97\u4e24\u4e2a\u4efb\u610f\u7ef4\u5ea6\u5411\u91cf\u4e4b\u95f4\u8ddd\u79bb\u7684\u51fd\u6570\u7684\u793a\u4f8b\uff1a<\/p>\n<\/blockquote>\n<pre><\/div><div class=\"code-block-extension-headerRight\"><span class=\"code-block-extension-lang\">typescript<\/span><div class=\"code-block-extension-copyCodeBtn\">\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div><code class=\"hljs language-typescript code-block-extension-codeShowNum\" lang=\"typescript\"><span class=\"code-block-extension-codeLine\" data-line-num=\"1\"><span class=\"hljs-keyword\">function<\/span> <span class=\"hljs-title function_\">calculateDistance<\/span>(<span class=\"hljs-params\">vector1: <span class=\"hljs-built_in\">number<\/span>[], vector2: <span class=\"hljs-built_in\">number<\/span>[]<\/span>): <span class=\"hljs-built_in\">number<\/span> {<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"2\">  <span class=\"hljs-keyword\">if<\/span> (vector1.<span class=\"hljs-property\">length<\/span> !== vector2.<span class=\"hljs-property\">length<\/span>) {<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"3\">    <span class=\"hljs-keyword\">throw<\/span> <span class=\"hljs-keyword\">new<\/span> <span class=\"hljs-title class_\">Error<\/span>(<span class=\"hljs-string\">'Vectors must have the same number of dimensions'<\/span>);<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"4\">  }<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"5\"><\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"6\">  <span class=\"hljs-keyword\">const<\/span> squaredDistances = vector1.<span class=\"hljs-title function_\">map<\/span>(<span class=\"hljs-function\">(<span class=\"hljs-params\">coord, index<\/span>) =&gt;<\/span> {<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"7\">    <span class=\"hljs-keyword\">const<\/span> diff = coord - vector2[index];<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"8\">    <span class=\"hljs-keyword\">return<\/span> diff * diff;<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"9\">  });<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"10\"><\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"11\">  <span class=\"hljs-keyword\">const<\/span> sumOfSquaredDistances = squaredDistances.<span class=\"hljs-title function_\">reduce<\/span>(<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"12\">    <span class=\"hljs-function\">(<span class=\"hljs-params\">accumulator, current<\/span>) =&gt;<\/span> accumulator + current,<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"13\">    <span class=\"hljs-number\">0<\/span><\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"14\">  );<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"15\"><\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"16\">  <span class=\"hljs-keyword\">return<\/span> <span class=\"hljs-title class_\">Math<\/span>.<span class=\"hljs-title function_\">sqrt<\/span>(sumOfSquaredDistances);<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"17\">}<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"18\"><\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"19\"><span class=\"hljs-comment\">\/\/ \u793a\u4f8b\u4f7f\u7528<\/span><\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"20\"><span class=\"hljs-keyword\">const<\/span> vector1 = [<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">3<\/span>];<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"21\"><span class=\"hljs-keyword\">const<\/span> vector2 = [<span class=\"hljs-number\">4<\/span>, <span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">6<\/span>];<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"22\"><span class=\"hljs-keyword\">const<\/span> distance = <span class=\"hljs-title function_\">calculateDistance<\/span>(vector1, vector2);<\/span>\n<span class=\"code-block-extension-codeLine\" data-line-num=\"23\"><span class=\"hljs-variable language_\">console<\/span>.<span class=\"hljs-title function_\">log<\/span>(distance);<\/span>\n<\/code><\/pre>\n<blockquote>\n<p>\u5728\u4e0a\u8ff0\u793a\u4f8b\u4e2d\uff0c<code>calculateDistance<\/code>&nbsp;\u51fd\u6570\u63a5\u53d7\u4e24\u4e2a\u53c2\u6570&nbsp;<code>vector1<\/code>&nbsp;\u548c&nbsp;<code>vector2<\/code>\uff0c\u5b83\u4eec\u5206\u522b\u4ee3\u8868\u4e24\u4e2a\u5411\u91cf\u3002\u51fd\u6570\u9996\u5148\u68c0\u67e5\u4e24\u4e2a\u5411\u91cf\u7684\u7ef4\u5ea6\u662f\u5426\u76f8\u540c\uff0c\u5982\u679c\u4e0d\u540c\u5219\u629b\u51fa\u9519\u8bef\u3002\u7136\u540e\uff0c\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u5750\u6807\u7684\u5dee\u503c\u7684\u5e73\u65b9\uff0c\u5e76\u5c06\u6240\u6709\u5e73\u65b9\u5dee\u503c\u76f8\u52a0\u3002\u6700\u540e\uff0c\u51fd\u6570\u8fd4\u56de\u5e73\u65b9\u548c\u7684\u5e73\u65b9\u6839\uff0c\u5373\u5411\u91cf\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002<\/p>\n<p>\u8bf7\u6ce8\u610f\uff0c\u8be5\u793a\u4f8b\u5047\u8bbe\u5411\u91cf\u7684\u7ef4\u5ea6\u662f\u76f8\u540c\u7684\uff0c\u5e76\u4e14\u5411\u91cf\u7684\u5750\u6807\u662f\u6570\u5b57\u7c7b\u578b\u3002\u5982\u679c\u9700\u8981\u5904\u7406\u5176\u4ed6\u7c7b\u578b\u6216\u8005\u66f4\u590d\u6742\u7684\u5411\u91cf\u64cd\u4f5c\uff0c\u53ef\u80fd\u9700\u8981\u8fdb\u884c\u9002\u5f53\u7684\u4fee\u6539\u3002<\/p>\n<\/blockquote>\n<p>\u4e0b\u4e00\u7bc7\u6587\u7ae0\u6211\u4eec\u4f1a\u7528 Node.js \u5b9e\u73b0\u80fd\u56de\u7b54\u6709\u5173\u676d\u5dde\u4e9a\u8fd0\u4f1a\u7684\u95ee\u9898\u7684\u95ee\u7b54\u7a0b\u5e8f\u3002<\/p>\n<h2 data-id=\"heading-5\">\u53c2\u8003\u94fe\u63a5<\/h2>\n<ul>\n<li><a href=\"https:\/\/link.juejin.cn?target=https%3A%2F%2Fgithub.com%2Fopenai%2Fopenai-cookbook%2Fblob%2Fmain%2Fexamples%2FQuestion_answering_using_embeddings.ipynb\" target=\"_blank\" title=\"https:\/\/github.com\/openai\/openai-cookbook\/blob\/main\/examples\/Question_answering_using_embeddings.ipynb\" ref=\"nofollow noopener noreferrer\" rel=\"noopener\">openai-cookbook\/examples\/Question_answering_using_embeddings.ipynb at main \u00b7 openai\/openai-cookbook (github.com)<\/a><\/li>\n<li><a href=\"https:\/\/link.juejin.cn?target=https%3A%2F%2Fgithub.com%2Fopenai%2Fopenai-cookbook%2Fblob%2Fmain%2Fexamples%2FEmbedding_Wikipedia_articles_for_search.ipynb\" target=\"_blank\" title=\"https:\/\/github.com\/openai\/openai-cookbook\/blob\/main\/examples\/Embedding_Wikipedia_articles_for_search.ipynb\" ref=\"nofollow noopener noreferrer\" rel=\"noopener\">openai-cookbook\/examples\/Question_answering_using_embeddings.ipynb at main \u00b7 openai\/openai-cookbook (github.com)<\/a><\/li>\n<li><a href=\"https:\/\/link.juejin.cn?target=https%3A%2F%2Fzhuanlan.zhihu.com%2Fp%2F63852350\" target=\"_blank\" title=\"https:\/\/zhuanlan.zhihu.com\/p\/63852350\" ref=\"nofollow noopener noreferrer\" rel=\"noopener\">\u4e00\u6761\u9f99\u641e\u5b9a\u60c5\u611f\u5206\u6790\uff1a\u6587\u672c\u9884\u5904\u7406\u3001\u52a0\u8f7d\u8bcd\u5411\u91cf\u3001\u642d\u5efaRNN &#8211; \u77e5\u4e4e (zhihu.com)<\/a><\/li>\n<li><a href=\"https:\/\/link.juejin.cn?target=https%3A%2F%2Fkawine.github.io%2Fblog%2Fnlp%2F2019%2F06%2F21%2Fword-analogies.html\" target=\"_blank\" title=\"https:\/\/kawine.github.io\/blog\/nlp\/2019\/06\/21\/word-analogies.html\" ref=\"nofollow noopener noreferrer\" rel=\"noopener\">Word Embedding Analogies: Understanding King &#8211; Man + Woman = Queen | Kawin Ethayarajh<\/a><\/li>\n<li><a href=\"https:\/\/link.juejin.cn?target=https%3A%2F%2Fanotherdatum.com%2Fgpt-3.html\" target=\"_blank\" title=\"https:\/\/anotherdatum.com\/gpt-3.html\" ref=\"nofollow noopener noreferrer\" rel=\"noopener\">GPT-3, a Giant Step for Deep Learning and&nbsp;NLP (anotherdatum.com)<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>LLMs\uff08\u8bed\u8a00\u6a21\u578b\uff09\u53ea\u80fd\u5229\u7528\u8bad\u7ec3\u6240\u7528\u6570\u636e\u96c6\u6765\u56de\u7b54\u95ee\u9898\uff0c\u672c\u6587\u5c06\u901a\u8fc7\u4f7f\u7528 Embedding \u641c\u7d22\u6765\u8ba9 GPT \u80fd\u591f\u56de\u7b54\u4e0e\u676d\u5dde\u4e9a\u8fd0\u4f1a\u76f8\u5173\u7684\u95ee\u9898\uff0c\u5e76\u6700\u7ec8\u5b9e\u73b0\u80fd\u65e0\u89c6 token \u9650\u5236\u65e0\u9650\u5bf9\u8bdd\u7684 AI \u52a9\u624b\u3002<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rank_math_title":"","rank_math_description":"","rank_math_focus_keyword":"","views":"8","footnotes":""},"categories":[3],"tags":[128,129,136,126,127],"collection":[],"class_list":["post-1354","post","type-post","status-publish","format-standard","hentry","category-fenlei2","tag-128","tag-129","tag-136","tag-gpt","tag-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1354","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/comments?post=1354"}],"version-history":[{"count":0,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1354\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/media?parent=1354"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/categories?post=1354"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/tags?post=1354"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/collection?post=1354"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}