LLM Prompt Datasets: An In-depth Analysis of 1.22 TB of Prompt Data and Linguistic Insights

Forum topic · ✨步子哥 · 2025-12-11

Summary

A research poster by Yuanming Zhang, Yan Lin, Arijit Khan, and Huaiyu Wan (Beijing Jiaotong University, Aalborg University, Bowling Green State University, dated October 10, 2025) presents the first comprehensive compilation of large language model prompt datasets. The authors collected 1.22 TB of data containing over 673 million prompt instances from 129 heterogeneous sources, including dataset platforms, academic publications, public repositories, and social media. They propose a hierarchical taxonomy categorizing prompts by downstream tasks, languages, engineering techniques, attributes, and modalities. Multi-level linguistic analysis across lexical, syntactic, and semantic dimensions on seven representative datasets reveals that prompts exhibit distinct compositional patterns, domain-specific variations, and more directive, task-oriented structures than general text. The team also introduces a prompt optimization method using syntactic embeddings: extracting POS and dependency features, identifying centroid representations, and guiding LLMs to rewrite prompts, improving output meaningfulness and quality. Datasets and code are publicly available.

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

Comprehensive collection of 1.22 TB of data, comprising 673M+ prompt instances from 129 heterogeneous sources:

Dataset platforms
Academic publications
Public repositories
Social media

Taxonomy

Hierarchical categorization of LLM prompt datasets by:

Downstream tasks
Languages
Engineering techniques
Attributes
Modalities

Analysis Methodology

Multi-level linguistic analysis across three dimensions on seven representative datasets:

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 in prompt construction across different 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

A novel prompt optimization method leveraging syntactic embeddings:

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

This results in improved meaningfulness and quality of model outputs.

Impact and Applications

First comprehensive compilation of prompt datasets
Provides a 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 (over 1.22 TB of curated prompt data):

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