ChatGPT技术总结2301_76965813ZIP1.zip 550.39KB 立即下载资源文件列表:ZIP 1.zip 大约有1个文件 1.docx 606.5KB 资源介绍: 最近ChatGPT可以说是火遍了全世界,作为由知名人工智能研究机构OpenAI于2022年11月30日发布的一个大型语言预训练模型,他的核心在于能够理解人类的自然语言,并使用贴近人类语言风格的方式来进行回复。模型开放使用以来,在人工智能领域引起了巨大的轰动,也成功火出了技术圈。从数据上看,ChatGPT用户数在5天内就达到了100万,2个月就达到了1亿;另外,在很多非人工智能领域,已经有机构在尝试用ChatGPT去做一些智能生成的事。例如财通证券发布了一篇由ChatGPT生成的行业研报,从研报的可读性和专业性上来看,虽然在细节上有很多需要推敲的地方,但是整体框架内容已经比较成熟。对于其他内容生产者来说,应用ChatGPT也能够提升个人的生产效率。 ChatGPT的强大能力是显而易见的,但对于人工智能领域不太熟悉的人,对这种黑盒的技术仍然会担忧或者不信任。恐惧通常来自于不了解,因此本文将为大家全面剖析ChatGPT的技术原理,尽量以简单通俗的文字为大家解惑。 通过本文,你可以有以下收获: 1、知道ChatGPT是什么 2、ChatGPT有哪些核心要素 3、ChatGPT能做哪些事 4、C <html xmlns="http://www.w3.org/1999/xhtml"><meta charset="utf-8"><meta name="generator" content="pdf2htmlEX"><meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"><link rel="stylesheet" href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css"><link rel="stylesheet" href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css"><link rel="stylesheet" href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/87684027/raw.css"><script src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/js/compatibility.min.js"></script><script src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/js/pdf2htmlEX.min.js"></script><script>try{pdf2htmlEX.defaultViewer = new pdf2htmlEX.Viewer({});}catch(e){}</script><div id="sidebar" style="display: none"><div id="outline"></div></div><div id="pf1" class="pf w0 h0" data-page-no="1"><div class="pc pc1 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/87684027/bg1.jpg"><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">最近<span class="_ _0"> </span><span class="ff2">ChatGPT<span class="_ _0"> </span></span>可以说是火遍了全世界,作为由知名人工智能研究机构<span class="_ _0"> </span><span class="ff2">OpenAI<span class="_ _0"> </span></span>于<span class="_ _0"> </span><span class="ff2">2022</span></div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">年<span class="_ _1"> </span><span class="ff2">11<span class="_ _1"> </span></span>月<span class="_ _1"> </span><span class="ff2">30<span class="_ _1"> </span></span>日发布的一个大型语言预训练模型,<span class="_ _2"></span>他的核心在于能够理解人类的自然语言,</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">并使用贴近人类语言风<span class="_ _3"></span>格的方式来进行回复。<span class="_ _3"></span>模型开放使用以来,在<span class="_ _3"></span>人工智能领域引起</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">了巨大的轰动,<span class="_ _4"></span>也成功火出了技术圈。<span class="_ _4"></span>从数据上看,<span class="_ _4"></span><span class="ff2">ChatGPT<span class="_ _0"> </span><span class="ff1">用户数在<span class="_ _0"> </span></span>5<span class="_ _0"> </span><span class="ff1">天内就达到了</span></span></div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">100<span class="_"> </span><span class="ff1">万<span class="_ _3"></span>,<span class="_ _5"></span></span>2<span class="_"> </span><span class="ff1">个<span class="_ _3"></span>月<span class="_ _5"></span>就<span class="_ _3"></span>达<span class="_ _5"></span>到<span class="_ _3"></span>了<span class="_ _6"> </span></span>1<span class="_"> </span><span class="ff1">亿<span class="_ _3"></span>;<span class="_ _5"></span>另<span class="_ _3"></span>外<span class="_ _5"></span>,<span class="_ _3"></span>在<span class="_ _3"></span>很<span class="_ _5"></span>多<span class="_ _3"></span>非<span class="_ _5"></span>人<span class="_ _3"></span>工<span class="_ _5"></span>智<span class="_ _3"></span>能<span class="_ _5"></span>领<span class="_ _3"></span>域<span class="_ _3"></span>,<span class="_ _5"></span>已<span class="_ _3"></span>经<span class="_ _5"></span>有<span class="_ _3"></span>机<span class="_ _5"></span>构<span class="_ _3"></span>在<span class="_ _5"></span>尝<span class="_ _3"></span>试<span class="_ _3"></span>用</span></div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">ChatGPT<span class="_ _0"> </span><span class="ff1">去做一些智能生成的事。例如财通证券发布了一篇由<span class="_ _0"> </span></span>ChatGPT<span class="_ _0"> </span><span class="ff1">生成的行业研</span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">报,从研报的可读性和<span class="_ _3"></span>专业性上来看,虽然在<span class="_ _3"></span>细节上有很多需要推敲<span class="_ _3"></span>的地方,但是整体</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">框架<span class="_ _3"></span>内容<span class="_ _3"></span>已<span class="_ _3"></span>经比<span class="_ _3"></span>较成<span class="_ _3"></span>熟<span class="_ _3"></span>。对<span class="_ _3"></span>于<span class="_ _3"></span>其他<span class="_ _3"></span>内容<span class="_ _3"></span>生<span class="_ _3"></span>产者<span class="_ _3"></span>来说<span class="_ _3"></span>,<span class="_ _3"></span>应用<span class="_ _6"> </span><span class="ff2">ChatGPT<span class="_ _0"> </span></span>也能<span class="_ _3"></span>够<span class="_ _3"></span>提升<span class="_ _3"></span>个人<span class="_ _3"></span>的</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">生产效率。</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">ChatGPT<span class="_ _0"> </span><span class="ff1">的强大能力是显而易见的,<span class="_ _7"></span>但对于人工智能领域不太熟悉的人,<span class="_ _7"></span>对这种黑盒的</span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">技<span class="_ _5"></span>术<span class="_ _5"></span>仍<span class="_ _8"></span>然<span class="_ _5"></span>会<span class="_ _5"></span>担<span class="_ _8"></span>忧<span class="_ _5"></span>或<span class="_ _5"></span>者<span class="_ _8"></span>不<span class="_ _5"></span>信<span class="_ _5"></span>任<span class="_ _8"></span>。<span class="_ _5"></span>恐<span class="_ _5"></span>惧<span class="_ _8"></span>通<span class="_ _5"></span>常<span class="_ _5"></span>来<span class="_ _8"></span>自<span class="_ _5"></span>于<span class="_ _5"></span>不<span class="_ _8"></span>了<span class="_ _5"></span>解<span class="_ _5"></span>,<span class="_ _8"></span>因<span class="_ _5"></span>此<span class="_ _5"></span><span class="ff3 sc1">本<span class="_ _8"></span>文<span class="_ _5"></span>将<span class="_ _8"></span>为<span class="_ _5"></span>大<span class="_ _8"></span>家<span class="_ _5"></span>全<span class="_ _8"></span>面<span class="_ _5"></span>剖<span class="_ _8"></span>析</span></div><div class="t m0 x1 h2 yc ff4 fs0 fc0 sc0 ls0 ws0">ChatGPT<span class="_ _0"> </span><span class="ff3 sc1">的技术原理<span class="_ _3"></span>,尽量以简<span class="_ _3"></span>单通俗的文字<span class="_ _3"></span>为大家解惑<span class="_ _3"></span>。</span></div><div class="t m0 x1 h2 yd ff3 fs0 fc0 sc1 ls0 ws0">通过本文,<span class="_ _3"></span>你可以有以<span class="_ _3"></span>下收获:</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">1<span class="ff1">、知道<span class="_ _0"> </span></span>ChatGPT<span class="_ _0"> </span><span class="ff1">是什么</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">2<span class="ff1">、</span>ChatGPT<span class="_ _0"> </span><span class="ff1">有哪些核心要素</span></div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">3<span class="ff1">、</span>ChatGPT<span class="_ _0"> </span><span class="ff1">能做哪些事</span></div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">4<span class="ff1">、</span>ChatGPT<span class="_ _0"> </span><span class="ff1">不能做哪些事</span></div><div class="t m0 x1 h2 y12 ff3 fs0 fc0 sc1 ls0 ws0">一、<span class="ff4 sc0">ChatGPT <span class="_ _1"> </span></span>是<span class="_ _3"></span>什么?</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">上文说到<span class="_ _0"> </span><span class="ff2">ChatGPT<span class="_ _0"> </span></span>实际上是一个大型语言预训练模型<span class="_ _9"></span>(即<span class="_ _0"> </span><span class="ff2">Large Language Model</span>,<span class="_ _9"></span>后</div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">面统一<span class="_ _3"></span>简称<span class="_ _6"> </span><span class="ff2">LLM</span>)。什<span class="_ _3"></span>么叫<span class="_ _6"> </span><span class="ff2">LLM</span>?<span class="ff4">LLM<span class="_"> </span><span class="ff3 sc1">指的是<span class="_ _3"></span>利<span class="_ _3"></span>用大<span class="_ _3"></span>量文<span class="_ _3"></span>本<span class="_ _3"></span>数据<span class="_ _3"></span>来<span class="_ _3"></span>训练<span class="_ _3"></span>的<span class="_ _3"></span>语言<span class="_ _3"></span>模<span class="_ _3"></span>型,</span></span></div><div class="t m0 x1 h2 y15 ff3 fs0 fc0 sc1 ls0 ws0">这种模型可<span class="_ _3"></span>以产生出强<span class="_ _3"></span>大的语言关<span class="_ _3"></span>联能力,能够从上下文中抽取出<span class="_ _3"></span>更多的信息<span class="_ _3"></span>。<span class="_ _a"></span><span class="ff1 sc0">其实语</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">言模型的研究从很早就<span class="_ _3"></span>开始了,随着算力的发<span class="_ _3"></span>展和数据规模的增长,<span class="_ _3"></span>语言模型的能力随</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">着模型参数量的增加而提升。<span class="_ _7"></span>下图分别展示了<span class="_ _0"> </span><span class="ff2">LLM<span class="_ _0"> </span></span>在参数量和数据量上的进化情况,<span class="_ _b"></span>其</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">中<span class="_ _3"></span>数<span class="_ _3"></span>据<span class="_ _3"></span>量<span class="_ _5"></span>图<span class="_ _3"></span>例<span class="_ _3"></span>展<span class="_ _3"></span>示<span class="_ _5"></span>的<span class="_ _3"></span>是<span class="_ _3"></span>模<span class="_ _5"></span>型<span class="_ _3"></span>在<span class="_ _3"></span>预<span class="_ _3"></span>训<span class="_ _5"></span>练<span class="_ _3"></span>过<span class="_ _3"></span>程<span class="_ _3"></span>中<span class="_ _5"></span>会<span class="_ _3"></span>见<span class="_ _3"></span>到<span class="_ _3"></span>的<span class="_ _6"> </span><span class="ff2">token<span class="_"> </span></span>数<span class="_ _3"></span>量<span class="_ _3"></span>,<span class="_ _5"></span>对<span class="_ _3"></span>于<span class="_ _3"></span>中<span class="_ _3"></span>文<span class="_ _5"></span>来<span class="_ _3"></span>说<span class="_ _3"></span>一<span class="_ _5"></span>个</div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">token<span class="_ _0"> </span><span class="ff1">就相当于一个中文字符。</span></div></div><div class="pi" data-data='{"ctm":[1.613555,0.000000,0.000000,1.613555,0.000000,0.000000]}'></div></div></html>