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href="https://code.visualstudio.com/"><i class="fa-fw fa-sharp fa-solid fa-star"></i><span> VSCode：微软旗下的跨平台代码编辑软件</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://mobaxterm.mobatek.net/"><i class="fa-fw fa-sharp fa-solid fa-star"></i><span> MobaXterm：超好用的全能远程终端</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://github.com/CopyTranslator/CopyTranslator"><i class="fa-fw fa-sharp fa-solid fa-star"></i><span> CopyTranslator：“复制即翻译”的外文辅助阅读翻译解决方案</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://www.zotero.org/"><i class="fa-fw fa-sharp fa-solid fa-star"></i><span> Zotero：便于收集、组织、引用、共享的文献管理工具</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://zealdocs.org/"><i class="fa-fw fa-sharp fa-solid fa-star"></i><span> Zeal：离线文档浏览器，其灵感来自 OS X平台上的 Dash，目前支持 Window 和 Liunx，基于 QT5</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://ditto-cp.sourceforge.io/"><i class="fa-fw fa-sharp fa-solid fa-star"></i><span> Ditto：强大的Windows剪贴板增强工具</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://wise-system-monitor.en.softonic.com/"><i class="fa-fw fa-sharp fa-solid fa-star"></i><span> Wise System Monitor：监控从系统到本地网络的所有运行情况</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://github.com/indiff/qttabbar"><i class="fa-fw fa-sharp fa-solid fa-star"></i><span> QtTabBar：在Windows资源管理器中使用多标签功能扩展工具</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://github.com/sentialx/multrin"><i class="fa-fw fa-sharp fa-solid fa-star-half-stroke"></i><span> Multrin：“窗口合并”辅助小工具</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://github.com/Wox-launcher/Wox"><i class="fa-fw fa-sharp fa-solid fa-star-half-stroke"></i><span> Wox & Everything：基于名称快速定位文件和文件夹的搜索工具</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://github.com/NickeManarin/ScreenToGif"><i class="fa-fw fa-regular fa-star"></i><span> ScreenToGif：快速录制屏幕指定区域并保存为动图文件</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="https://mathpix.com/"><i class="fa-fw fa-regular fa-star"></i><span> Mathpix Snipping：识别数学公式并转换成LaTeX</span></a></li><li><a class="site-page" target="_blank" rel="noopener" href="http://www.uderzo.it/main_products/space_sniffer/index.html"><i class="fa-fw fa-regular fa-star"></i><span> Space Sniffer：磁盘空间分析工具</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> 友链</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> 关于</span></a></div></div><div id="toggle-menu"><a class="site-page"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="site-info"><h1 id="site-title">LOUIS' BLOG</h1><div 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title="Arxiv每日速递(2025-01-08)">Arxiv每日速递(2025-01-08)</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-01-08T01:06:39.130Z" title="发表于 2025-01-08 09:06:39">2025-01-08</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E9%98%85%E8%AF%BB%E7%AC%94%E8%AE%B0/">阅读笔记</a></span></div><div class="content">本篇博文主要展示每日从Arxiv论文网站获取的最新论文列表，以自然语言处理、信息检索、计算机视觉等类目进行划分。
统计
今日共更新632篇论文，其中：
自然语言处理95篇
信息检索21篇
计算机视觉177篇
自然语言处理
1. 【2501.03226】BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning
链接：https://arxiv.org/abs/2501.03226
作者：Beichen Zhang,Yuhong Liu,Xiaoyi Dong,Yuhang Zang,Pan Zhang,Haodong Duan,Yuhang Cao,Dahua Lin,Jiaqi Wang
类目：Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
关键词：Cutting-edge la ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2025/01/01/%E5%9B%BE%E8%A7%A3%EF%BC%9ALLM%E6%98%AF%E6%80%8E%E4%B9%88%E9%80%9A%E8%BF%87PPO%E4%BC%98%E5%8C%96%E5%81%8F%E5%A5%BD%E7%9A%84%EF%BC%9F.html" title="图解：LLM是怎么通过PPO优化偏好的？"> <img class="post_bg" src="https://cdn.jsdelivr.net/gh/isLouisHsu/resource@master/blog_resource/misc/cover/ww3.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="图解：LLM是怎么通过PPO优化偏好的？"></a></div><div class="recent-post-info"><a class="article-title" href="/2025/01/01/%E5%9B%BE%E8%A7%A3%EF%BC%9ALLM%E6%98%AF%E6%80%8E%E4%B9%88%E9%80%9A%E8%BF%87PPO%E4%BC%98%E5%8C%96%E5%81%8F%E5%A5%BD%E7%9A%84%EF%BC%9F.html" title="图解：LLM是怎么通过PPO优化偏好的？">图解：LLM是怎么通过PPO优化偏好的？</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-01-01T10:59:45.000Z" title="发表于 2025-01-01 18:59:45">2025-01-01</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a></span></div><div class="content">TODO: 结合RL原理、图示、代码，对PPO计算流程加以说明
ϕ=arg maxϕEs∼p(s;ϕ)12min(∣∣rt+γVπ(st+1)−V~π(st)∣∣22‾,∣∣rt+γVπ(st+1)−clip(V~π(st),Vminπ,Vmaxπ)∣∣22‾)\phi = \argmax_{\phi} E_{s \sim p(s;\phi)} \frac{1}{2} \min \left(
\underline{
|| r_t + \gamma V^{\pi}(s_{t+1}) - \tilde{V}^{\pi}(s_t) ||_2^2
},
\underline{
|| r_t + \gamma V^{\pi}(s_{t+1}) - \text{clip}(
\tilde{V}^{\pi}(s_t), V^{\pi}_{min}, V^{\pi}_{max}
) ||_2^2
}
\right)
ϕ=ϕargmaxEs∼p(s;ϕ)21min(∣∣rt+γVπ(st+1)−V~π(st) ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2024/02/03/Stable%20Diffusion%20%E6%8F%90%E7%A4%BA%E8%AF%8D%E6%8C%87%E5%8D%97%E4%B9%A6.html" title="🎨 Stable Diffusion 提示词指南书"> <img class="post_bg" src="https://huggingface.co/blog/assets/98_stable_diffusion/stable_diffusion_12_1.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="🎨 Stable Diffusion 提示词指南书"></a></div><div class="recent-post-info"><a class="article-title" href="/2024/02/03/Stable%20Diffusion%20%E6%8F%90%E7%A4%BA%E8%AF%8D%E6%8C%87%E5%8D%97%E4%B9%A6.html" title="🎨 Stable Diffusion 提示词指南书">🎨 Stable Diffusion 提示词指南书</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-02-03T06:57:45.000Z" title="发表于 2024-02-03 14:57:45">2024-02-03</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/AIGC/">AIGC</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/categories/AIGC/%E5%A4%9A%E6%A8%A1%E6%80%81/">多模态</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/categories/AIGC/%E5%A4%9A%E6%A8%A1%E6%80%81/%E6%96%87%E7%94%9F%E5%9B%BE/">文生图</a></span></div><div class="content">封面图来自 Stable Diffusion with 🧨 Diffusers
</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2023/10/22/Transformer%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%BD%8D%E7%BD%AE%E7%BC%96%E7%A0%81%E4%B8%8E%E9%95%BF%E5%BA%A6%E5%A4%96%E6%8E%A8.html" title="Transformer语言模型的位置编码与长度外推"> <img class="post_bg" src="https://cdn.jsdelivr.net/gh/isLouisHsu/resource@master/blog_resource/misc/cover/city.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="Transformer语言模型的位置编码与长度外推"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/10/22/Transformer%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%BD%8D%E7%BD%AE%E7%BC%96%E7%A0%81%E4%B8%8E%E9%95%BF%E5%BA%A6%E5%A4%96%E6%8E%A8.html" title="Transformer语言模型的位置编码与长度外推">Transformer语言模型的位置编码与长度外推</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-10-22T14:55:45.000Z" title="发表于 2023-10-22 22:55:45">2023-10-22</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a></span></div><div class="content">TL;DR
Transformer模型为了处理序列的位置信息，引入了位置编码(Position Embedding, PE)。常见的位置编码方案有绝对位置编码(Absolute Position Embedding)、相对位置编码(Relative Position Embedding)和旋转位置编码(Rotary Position Embedding, RoPE)。
绝对位置编码：使用三角函数式位置编码，如Sinusoidal APE，将位置信息累加到输入序列的元素向量中，有助于模型感知输入的顺序。
相对位置编码：不为每个元素引入特定的位置表征，而是关注元素之间的相对位置关系。在NeZha、DeBERTa等模型中使用，有更强的长距离依赖建模能力。
旋转位置编码：是在绝对位置编码的基础上引入的一种改进，采用了“绝对位置编码方式实现的相对位置编码”，在实验中表现出更好的性能。
针对模型处理长文本的问题，提出了几种长度外推方法：
线性内插（Linear Interpolation）：通过减小位置精度，使得可表示范围内容纳更多位置，但可能需要进一步预训练适配。
NTK-Scaling ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2023/09/22/vLLM%EF%BC%9A%E5%88%A9%E7%94%A8%E5%88%86%E9%A1%B5%E7%BC%93%E5%AD%98%E5%92%8C%E5%BC%A0%E9%87%8F%E5%B9%B6%E8%A1%8C%E6%8F%90%E9%AB%98%E5%A4%A7%E6%A8%A1%E5%9E%8B2~4x%E6%8E%A8%E7%90%86%E9%80%9F%E5%BA%A6.html" title="vLLM：利用分页缓存和张量并行提高大模型2~4x推理速度"> <img class="post_bg" src="https://cdn.jsdelivr.net/gh/isLouisHsu/resource@master/blog_resource/misc/cover/ww5.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="vLLM：利用分页缓存和张量并行提高大模型2~4x推理速度"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/09/22/vLLM%EF%BC%9A%E5%88%A9%E7%94%A8%E5%88%86%E9%A1%B5%E7%BC%93%E5%AD%98%E5%92%8C%E5%BC%A0%E9%87%8F%E5%B9%B6%E8%A1%8C%E6%8F%90%E9%AB%98%E5%A4%A7%E6%A8%A1%E5%9E%8B2~4x%E6%8E%A8%E7%90%86%E9%80%9F%E5%BA%A6.html" title="vLLM：利用分页缓存和张量并行提高大模型2~4x推理速度">vLLM：利用分页缓存和张量并行提高大模型2~4x推理速度</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-09-22T14:55:45.000Z" title="发表于 2023-09-22 22:55:45">2023-09-22</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a></span></div><div class="content">TL;DR
GPT和PaLM等大型语言模型（LLM）能准确地理解自然语言指令并生成准确、富有创意的文本响应，可以作为编程助手、通用聊天机器人等新型应用的强力底座。但这些强大的模型依赖庞大的计算和高昂的运行成本，实际部署时对请求并发量和资源利用效率提出了关键性的挑战。伯克利大学研究人员受虚拟内存系统中分页（paging）技术启发，设计了PagedAttention，通过对显存的分块管理，实现了自注意力机制（self attention mechanism）中KV缓存的几乎零显存浪费和灵活的资源共享（如下图），并结合张量并行（tensor parallel）技术提高显卡设备计算核心的利用率，极大地加速了模型推理速度。与其他SOTA部署方案相比，提高了2~4x的吞吐量^1。
上效果图感受一下vLLM的加速效果，图中曲线颜色表示不同框架，蓝线是vLLM，横轴表示每秒请求数量（req/s），纵轴是延迟量化指标，即平均每个token生成时长（s/token）。可以看到vLLM可以在更高的并发请求量下保持推理速度，表示用户可以在更短的时间内获得他们的请求响应，从而提高了用户体验。
首页：htt ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2023/09/06/Prompt%EF%BC%9A%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E6%89%A7%E8%A1%8C%E6%8C%87%E5%8D%97.html" title="Prompt：大语言模型的执行指南"> <img class="post_bg" src="https://cdn.jsdelivr.net/gh/isLouisHsu/resource@master/blog_resource/misc/cover/ww6.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="Prompt：大语言模型的执行指南"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/09/06/Prompt%EF%BC%9A%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E6%89%A7%E8%A1%8C%E6%8C%87%E5%8D%97.html" title="Prompt：大语言模型的执行指南">Prompt：大语言模型的执行指南</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-09-06T14:45:45.000Z" title="发表于 2023-09-06 22:45:45">2023-09-06</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a></span></div><div class="content">
TL;DR
提示词(Prompt)是指由用户或系统提供给大语言模型(Large Language Model, LLM)的一段文字或问题，模型在这些给定信息(又称上下文)下，生成相关的回复或文本。Prompt作为大语言模型的执行指南，其好坏直接影响大语言模型的生成效果，但问题在于不知道如何创作高质量的 Prompt，比如：完成一个Prompt需要哪些要素？这些要素要用什么样的话术来描述？用何种顺序或结构来组织多个要素？写完Prompt后，怎么评估其有效性？如果效果不好，可以从哪些方面进行改进？本文就这些问题，整理了一些Prompt工程相关的资料，希望通过吸取他人经验、结合个人实践经历，总结创作Prompt工程的方法论。
在本文中，可以了解到以下内容：
TL;DR
问题：大语言模型的能力限制
创作原则：六条来自OpenAI的GPT最佳实践
结构化Prompt：Prompt工程师的“八股文”
鲜明的特点和优势
要素与组织结构
创作的合理顺序
构建结构化Prompt真正重要的事
自动化开发和调优
最佳实践
思考：再看结构化Prompt
几种Prompt的设计策略
启动大语言模型能力 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2023/09/03/%E3%80%90%E8%BD%AC%E8%BD%BD%E3%80%91%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E5%9C%A81688%E7%94%B5%E5%95%86%E5%9C%BA%E6%99%AF%E7%9A%84%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5.html" title="【转载】大语言模型在1688电商场景的算法实践"> <img class="post_bg" src="https://cdn.jsdelivr.net/gh/isLouisHsu/resource@master/blog_resource/misc/cover/default.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【转载】大语言模型在1688电商场景的算法实践"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/09/03/%E3%80%90%E8%BD%AC%E8%BD%BD%E3%80%91%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E5%9C%A81688%E7%94%B5%E5%95%86%E5%9C%BA%E6%99%AF%E7%9A%84%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5.html" title="【转载】大语言模型在1688电商场景的算法实践">【转载】大语言模型在1688电商场景的算法实践</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-09-03T15:35:45.000Z" title="发表于 2023-09-03 23:35:45">2023-09-03</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a></span></div><div class="content">
转载自闲记算法 - lonePatient
</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2023/05/07/%E3%80%90%E6%A2%B3%E7%90%86%E3%80%91%E9%99%86%E5%A5%87%E6%9C%80%E6%96%B0%E6%BC%94%E8%AE%B2%E5%AE%9E%E5%BD%95%EF%BC%9A%E6%88%91%E7%9A%84%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B8%96%E7%95%8C%E8%A7%82%20.html" title="【梳理】陆奇最新演讲实录：我的大模型世界观"> <img class="post_bg" src="https://cdn.jsdelivr.net/gh/isLouisHsu/resource@master/blog_resource/misc/cover/default.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【梳理】陆奇最新演讲实录：我的大模型世界观"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/05/07/%E3%80%90%E6%A2%B3%E7%90%86%E3%80%91%E9%99%86%E5%A5%87%E6%9C%80%E6%96%B0%E6%BC%94%E8%AE%B2%E5%AE%9E%E5%BD%95%EF%BC%9A%E6%88%91%E7%9A%84%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B8%96%E7%95%8C%E8%A7%82%20.html" title="【梳理】陆奇最新演讲实录：我的大模型世界观">【梳理】陆奇最新演讲实录：我的大模型世界观</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-05-07T11:07:45.000Z" title="发表于 2023-05-07 19:07:45">2023-05-07</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a></span></div><div class="content">TL;DR
我们面临这样一个时代的机会。它既是机会，也是挑战。我们建议你就这个机会做全方位思考。 —— 陆奇
陆奇是中国著名的企业家和技术领袖，现任奇绩创坛董事长。他曾经担任过百度公司CEO和微软公司全球副总裁等职务，是中国互联网和人工智能领域的重要人物之一。陆奇在百度任职期间，带领公司实现了从搜索引擎到人工智能的转型，并推动了百度在人工智能领域的创新和发展。他在人工智能、大数据和云计算等领域拥有深厚的技术背景和丰富的管理经验，被誉为“中国人工智能第一人”。2018年，陆奇创办了奇绩创坛，旨在为创新企业提供技术、资金和市场等全方位支持，推动中国科技创新的发展。奇绩创坛已经成为中国创新创业领域的重要力量，陆奇也因此被誉为中国创新创业领域的领军人物之一。
面对当前全世界对大模型的高度关注，他做了“我的大模型世界观”的演讲，其中分享了他对大模型时代的宏观思考.他指出，技术的进步驱动着人类社会结构和范式的不断更迭。我们目前正处于一个新范式的重要拐点，其中包括信息生态系统、模型系统和行动系统三个体系的组合。我们已经走过了信息无处不在的互联网范式阶段。在当前阶段中，“模型”知识无处不在，基于 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/2023/03/27/%E3%80%90%E8%BD%AC%E8%BD%BD%E3%80%91ChatGPT%20%E6%A0%87%E6%B3%A8%E6%8C%87%E5%8D%97%EF%BC%9A%E4%BB%BB%E5%8A%A1%E3%80%81%E6%95%B0%E6%8D%AE%E4%B8%8E%E8%A7%84%E8%8C%83.html" title="【转载】ChatGPT 标注指南：任务、数据与规范"> <img class="post_bg" src="https://openaicom.imgix.net/8d14e8f0-e267-4b8b-a9f2-a79120808f5a/chatgpt.jpg?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2048%2C2048&w=3200" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【转载】ChatGPT 标注指南：任务、数据与规范"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/03/27/%E3%80%90%E8%BD%AC%E8%BD%BD%E3%80%91ChatGPT%20%E6%A0%87%E6%B3%A8%E6%8C%87%E5%8D%97%EF%BC%9A%E4%BB%BB%E5%8A%A1%E3%80%81%E6%95%B0%E6%8D%AE%E4%B8%8E%E8%A7%84%E8%8C%83.html" title="【转载】ChatGPT 标注指南：任务、数据与规范">【转载】ChatGPT 标注指南：任务、数据与规范</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-03-27T14:35:45.000Z" title="发表于 2023-03-27 22:35:45">2023-03-27</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a></span></div><div class="content">TL;DR
转载自ChatGPT 标注指南：任务、数据与规范 - Yam
ChatGPT 刚刚出来时，业内人士一致认为高质量的数据是一个非常关键的因素。且不论这个结论在 ChatGPT 这里是否正确，但高质量的数据对模型大有裨益却是公认的。而且，我们也可以从公开的 InstructGPT 标注指南中对此窥探一二。本文主要就围绕这份指南进行介绍，有点标题党了，但是考虑到 ChatGPT 和 InstructGPT 是兄弟关系，我们有理由相信 ChatGPT 的标注也是基于 InstructGPT 给出的指南进行的。当然不一定是全部，但至少我们可以从中学习和借鉴一些东西，是有此文。
本文主要包括以下几个方面内容：
总体介绍：我们首先会简单介绍 ChatGPT 训练过程中的几个涉及到标注的任务，清楚了任务才能更好地了解标注。然后从宏观角度统领几个方面的设计，包括数据、人员、规范等。
标注数据：包括数据收集、数据分析、数据预处理等。
标注人员：包括人员筛选、人员特征、满意度调查等。
标注规范：包括关键指标、标注方法细则、标注示例、FAQ 等。
多想一点：主要是个人的一些补充和思考。
总 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/2023/03/26/%E3%80%90%E8%BD%AC%E8%BD%BD%E3%80%91%E9%80%9A%E5%90%91AGI%E4%B9%8B%E8%B7%AF%EF%BC%9A%E5%A4%A7%E5%9E%8B%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%EF%BC%88LLM%EF%BC%89%E6%8A%80%E6%9C%AF%E7%B2%BE%E8%A6%81.html" title="【转载】通向AGI之路：大型语言模型（LLM）技术精要"> <img class="post_bg" src="https://picx.zhimg.com/v2-8eab4d7521252501ef5d836e786c45c6_1440w.jpg?source=172ae18b" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【转载】通向AGI之路：大型语言模型（LLM）技术精要"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/03/26/%E3%80%90%E8%BD%AC%E8%BD%BD%E3%80%91%E9%80%9A%E5%90%91AGI%E4%B9%8B%E8%B7%AF%EF%BC%9A%E5%A4%A7%E5%9E%8B%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%EF%BC%88LLM%EF%BC%89%E6%8A%80%E6%9C%AF%E7%B2%BE%E8%A6%81.html" title="【转载】通向AGI之路：大型语言模型（LLM）技术精要">【转载】通向AGI之路：大型语言模型（LLM）技术精要</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-03-26T14:55:45.000Z" title="发表于 2023-03-26 22:55:45">2023-03-26</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a></span></div><div class="content">
转载自通向AGI之路：大型语言模型（LLM）技术精要 - 知乎/张俊林
目前规模最大的LLM模型，几乎清一色都是类似GPT 3.0这种“自回归语言模型+Prompting”模式的，比如GPT 3、PaLM、GLaM、Gopher、Chinchilla、MT-NLG、LaMDA等，没有例外。为什么会这样呢？
自然语言生成任务，在表现形式上可以兼容自然语言理解任务，若反过来，则很难做到这一点。这样的好处是：同一个LLM生成模型，可以解决几乎所有NLP问题。而如果仍然采取Bert模式，则这个LLM模型无法很好处理生成任务。既然这样，我们当然倾向于使用生成模型，这是一个原因。
现在已有研究（参考：On the Role of Bidirectionality in Language Model Pre-Training）证明：如果是以fine-tuning方式解决下游任务，Bert模式的效果优于GPT模式；若是以zero shot/few shot prompting这种模式解决下游任务，则GPT模式效果要优于Bert模式。这说明了，生成模型更容易做好zero shot/few sho ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/">2</a><a class="page-number" href="/page/3/">3</a><a class="extend next" rel="next" href="/page/2/"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="card-info-avatar is-center"><img class="avatar-img" src="/img/touxiang.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/><div class="author-info__name">徐耀彬</div><div class="author-info__description">💭这个人很懒，什么都没有留下</div></div><div class="card-info-data"><div class="card-info-data-item is-center"><a href="/archives/"><div class="headline">文章</div><div 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