大语言模型提示数据集：深入分析与见解
✨步子哥 (steper) •
2025年12月11日 04:48
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<title>大语言模型提示数据集：深入分析与见解</title>
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<body>
<div class="poster-container">
<!-- 标题部分 -->
<div class="header">
<h1 class="title">大语言模型提示数据集：深入分析与见解</h1>
<p class="authors">张元明*，林燕*，阿里吉特·汗†，万怀宇</p>
<p class="affiliations">北京交通大学，奥尔堡大学，鲍林格林州立大学</p>
<p class="date">2025年10月10日</p>
</div>
<!-- 摘要/引言部分 -->
<div class="section">
<h2 class="section-title">
<span class="material-icons">description</span>
摘要
</h2>
<div class="section-content">
提示是一种自然语言指令，为大语言模型（LLM）定义特定任务，并作为人机交互的主要界面。随着大语言模型的广泛部署，各种提示数据集正从GitHub和社交媒体等平台涌现。这些数据集涵盖广泛的应用和内容类型，促进了更广泛的大语言模型使用和改进的提示工程。
</div>
</div>
<!-- 数据收集部分 -->
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<h2 class="section-title">
<span class="material-icons">storage</span>
数据收集
</h2>
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<p>全面收集了 <span class="stat-highlight">1.22 TB</span> 的数据，包含来自 <span class="stat-highlight">129</span> 个异构来源的 <span class="stat-highlight">673M+</span> 提示实例：</p>
<div class="data-sources">
<div class="source-item">
<span class="material-icons">dataset</span>
数据集平台
</div>
<div class="source-item">
<span class="material-icons">school</span>
学术出版物
</div>
<div class="source-item">
<span class="material-icons">code</span>
公共存储库
</div>
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<span class="material-icons">forum</span>
社交媒体
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<!-- 分类法部分 -->
<div class="section">
<h2 class="section-title">
<span class="material-icons">account_tree</span>
分类法
</h2>
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<p>大语言模型提示数据集的分层分类，按以下方面：</p>
<div class="taxonomy-list">
<div class="taxonomy-item">下游任务</div>
<div class="taxonomy-item">语言</div>
<div class="taxonomy-item">工程技术</div>
<div class="taxonomy-item">属性</div>
<div class="taxonomy-item">模态</div>
</div>
</div>
</div>
<!-- 分析方法部分 -->
<div class="section">
<h2 class="section-title">
<span class="material-icons">analytics</span>
分析方法
</h2>
<div class="section-content">
<p>对七个代表性数据集进行三个维度的多层次语言分析：</p>
<div class="analysis-levels">
<div class="analysis-level">
<div class="analysis-level-title">词汇层面</div>
<div>标记分布、词汇分析</div>
</div>
<div class="analysis-level">
<div class="analysis-level-title">句法层面</div>
<div>依存解析、词性标注、TF-IDF</div>
</div>
<div class="analysis-level">
<div class="analysis-level-title">语义层面</div>
<div>主题建模、语义相似度</div>
</div>
</div>
</div>
</div>
<!-- 主要发现部分 -->
<div class="section">
<h2 class="section-title">
<span class="material-icons">lightbulb</span>
主要发现
</h2>
<div class="section-content">
<div class="findings-list">
<div class="finding-item">
<span class="material-icons">check_circle</span>
<div>与其他文本语料库相比，提示表现出独特的组合模式</div>
</div>
<div class="finding-item">
<span class="material-icons">check_circle</span>
<div>不同应用中提示构建的领域特定变化</div>
</div>
<div class="finding-item">
<span class="material-icons">check_circle</span>
<div>独特的语言特性将提示与文学和网络内容区分开来</div>
</div>
<div class="finding-item">
<span class="material-icons">check_circle</span>
<div>提示往往比一般文本更具指导性和任务导向性</div>
</div>
</div>
</div>
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<!-- 优化方法部分 -->
<div class="section">
<h2 class="section-title">
<span class="material-icons">tune</span>
优化方法
</h2>
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<p>利用句法嵌入的新颖提示优化方法：</p>
<div class="optimization-diagram">
<div class="diagram-step">
<span class="material-icons">text_fields</span>
<div>提取词性与依存特征</div>
</div>
<div class="diagram-arrow">→</div>
<div class="diagram-step">
<span class="material-icons">hub</span>
<div>识别质心表示</div>
</div>
<div class="diagram-arrow">→</div>
<div class="diagram-step">
<span class="material-icons">edit</span>
<div>指导大语言模型重写提示</div>
</div>
</div>
<p>提高了模型输出的意义和质量。</p>
</div>
</div>
<!-- 影响与应用部分 -->
<div class="section">
<h2 class="section-title">
<span class="material-icons">insights</span>
影响与应用
</h2>
<div class="section-content">
<div class="findings-list">
<div class="finding-item">
<span class="material-icons">star</span>
<div>首次全面编译提示数据集</div>
</div>
<div class="finding-item">
<span class="material-icons">star</span>
<div>为系统提示工程研究提供基础</div>
</div>
<div class="finding-item">
<span class="material-icons">star</span>
<div>实现更有效的提示选择和优化</div>
</div>
<div class="finding-item">
<span class="material-icons">star</span>
<div>促进大语言模型在各种应用中的广泛部署</div>
</div>
</div>
</div>
</div>
<!-- 资源部分 -->
<div class="section">
<h2 class="section-title">
<span class="material-icons">folder_open</span>
资源
</h2>
<div class="section-content">
<p>数据集和代码可供研究使用：</p>
<a href="https://anonymous.4open.science/r/LLM-Prompt-Datasets-7416" class="resource-link" target="_blank">
<span class="material-icons">link</span>
https://anonymous.4open.science/r/LLM-Prompt-Datasets-7416
</a>
<p style="margin-top: 10px;">超过1.22 TB的精选提示数据供研究使用</p>
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