Large Language Model Prompt Datasets: An In-depth Analysis and Insights

Authors: Yuanming Zhang*, Yan Lin*, Arijit Khan†, Huaiyu Wan Affiliations: Beijing Jiaotong University, Aalborg University, Bowling Green State University Date: October 10, 2025

Abstract

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.

Data Collection

The study compiles a comprehensive corpus:

• 1.22 TB of data
• 673M+ prompt instances
• 129 heterogeneous sources, including:
• Dataset platforms
• Academic publications
• Public repositories
• Social media

Taxonomy

LLM prompt datasets are hierarchically categorized by:

• Downstream tasks
• Languages
• Engineering techniques
• Attributes
• Modalities

Analysis Methodology

A multi-level linguistic analysis is performed across three dimensions on seven representative datasets:

| Level | Techniques | |---|---| | Lexical | Token distribution, vocabulary analysis | | Syntactic | Dependency parsing, POS tagging, TF-IDF | | Semantic | Topic modeling, semantic similarity |

Key Findings

• Prompts exhibit distinct compositional patterns compared to other text corpora.
• Domain-specific variations exist in prompt construction across applications.
• Unique linguistic properties distinguish prompts from literature and web content.
• Prompts tend to be more directive and task-oriented than general text.

Optimization Approach

The authors propose a novel prompt optimization method leveraging syntactic embeddings:

1. Extract POS and dependency features 2. Identify centroid representations 3. Guide LLMs to rewrite prompts

This process results in improved meaningfulness and quality of model outputs.

Impact and Applications

• First comprehensive compilation of prompt datasets
• Foundation for systematic prompt engineering research
• Enables more effective prompt selection and refinement
• Facilitates broader LLM deployment across diverse applications

Resources

Datasets and code are available for research use:

https://anonymous.4open.science/r/LLM-Prompt-Datasets-7416