Large Language Model Prompt Datasets: An In-depth Analysis and Insights
未知用户 (steper) •
2025年12月11日 03:49
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<h1 class="title">Large Language Model Prompt Datasets: An In-depth Analysis and Insights</h1>
<p class="authors">Yuanming Zhang*, Yan Lin*, Arijit Khan†, Huaiyu Wan</p>
<p class="affiliations">Beijing Jiaotong University, Aalborg University, Bowling Green State University</p>
<p class="date">October 10, 2025</p>
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<!-- Abstract/Introduction Section -->
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<h2 class="section-title">
<span class="material-icons">description</span>
Abstract
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<div class="section-content">
A prompt is a natural language instruction that defines a specific task for a large language model (LLM) and serves as the primary interface for human-LLM interaction. With the growing deployment of LLMs, diverse prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a wide array of applications and content types, facilitating both broader LLM utilization and improved prompt engineering.
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<!-- Data Collection Section -->
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<h2 class="section-title">
<span class="material-icons">storage</span>
Data Collection
</h2>
<div class="section-content">
<p>Comprehensive collection of <span class="stat-highlight">1.22 TB</span> of data, comprising <span class="stat-highlight">673M+</span> prompt instances from <span class="stat-highlight">129</span> heterogeneous sources:</p>
<div class="data-sources">
<div class="source-item">
<span class="material-icons">dataset</span>
Dataset Platforms
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<div class="source-item">
<span class="material-icons">school</span>
Academic Publications
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<div class="source-item">
<span class="material-icons">code</span>
Public Repositories
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<div class="source-item">
<span class="material-icons">forum</span>
Social Media
</div>
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<!-- Taxonomy Section -->
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<h2 class="section-title">
<span class="material-icons">account_tree</span>
Taxonomy
</h2>
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<p>Hierarchical categorization of LLM prompt datasets by:</p>
<div class="taxonomy-list">
<div class="taxonomy-item">Downstream Tasks</div>
<div class="taxonomy-item">Languages</div>
<div class="taxonomy-item">Engineering Techniques</div>
<div class="taxonomy-item">Attributes</div>
<div class="taxonomy-item">Modalities</div>
</div>
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<!-- Analysis Methodology Section -->
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<h2 class="section-title">
<span class="material-icons">analytics</span>
Analysis Methodology
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<p>Multi-level linguistic analysis across three dimensions on seven representative datasets:</p>
<div class="analysis-levels">
<div class="analysis-level">
<div class="analysis-level-title">Lexical</div>
<div>Token distribution, vocabulary analysis</div>
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<div class="analysis-level">
<div class="analysis-level-title">Syntactic</div>
<div>Dependency parsing, POS tagging, TF-IDF</div>
</div>
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<div class="analysis-level-title">Semantic</div>
<div>Topic modeling, semantic similarity</div>
</div>
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<!-- Key Findings Section -->
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<h2 class="section-title">
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Key Findings
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<div class="findings-list">
<div class="finding-item">
<span class="material-icons">check_circle</span>
<div>Prompts exhibit distinct compositional patterns compared to other text corpora</div>
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<div class="finding-item">
<span class="material-icons">check_circle</span>
<div>Domain-specific variations in prompt construction across different applications</div>
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<div class="finding-item">
<span class="material-icons">check_circle</span>
<div>Unique linguistic properties distinguish prompts from literature and web content</div>
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<div class="finding-item">
<span class="material-icons">check_circle</span>
<div>Prompts tend to be more directive and task-oriented than general text</div>
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<!-- Optimization Approach Section -->
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<h2 class="section-title">
<span class="material-icons">tune</span>
Optimization Approach
</h2>
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<p>Novel prompt optimization method leveraging syntactic embeddings:</p>
<div class="optimization-diagram">
<div class="diagram-step">
<span class="material-icons">text_fields</span>
<div>Extract POS & Dependency Features</div>
</div>
<div class="diagram-arrow">→</div>
<div class="diagram-step">
<span class="material-icons">hub</span>
<div>Identify Centroid Representation</div>
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<div class="diagram-arrow">→</div>
<div class="diagram-step">
<span class="material-icons">edit</span>
<div>Guide LLMs to Rewrite Prompts</div>
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<p>Results in improved meaningfulness and quality of model outputs.</p>
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<!-- Impact and Applications Section -->
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<h2 class="section-title">
<span class="material-icons">insights</span>
Impact and Applications
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<div class="section-content">
<div class="findings-list">
<div class="finding-item">
<span class="material-icons">star</span>
<div>First comprehensive compilation of prompt datasets</div>
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<div class="finding-item">
<span class="material-icons">star</span>
<div>Provides foundation for systematic prompt engineering research</div>
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<div class="finding-item">
<span class="material-icons">star</span>
<div>Enables more effective prompt selection and refinement</div>
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<div class="finding-item">
<span class="material-icons">star</span>
<div>Facilitates broader LLM deployment across diverse applications</div>
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<!-- Resources Section -->
<div class="section">
<h2 class="section-title">
<span class="material-icons">folder_open</span>
Resources
</h2>
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<p>Datasets and code available for research use:</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;">Over 1.22 TB of curated prompt data for research use</p>
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