LLM Prompt Datasets: In-Depth Analysis and Insights

Forum topic · ✨步子哥 · 2025-12-11

Summary

This post presents a large-scale study of prompt datasets for large language models (LLMs), compiled by researchers from Beijing Jiaotong University, Aalborg University, and Bowling Green State University. The team aggregated over 1.22 TB of data containing more than 673 million prompt instances from 129 heterogeneous sources, including dataset platforms, academic publications, public repositories, and social media. They introduce a hierarchical taxonomy of prompt datasets organized by downstream task, language, engineering technique, attributes, and modality. Multi-level linguistic analysis of seven representative datasets—covering lexical, syntactic, and semantic dimensions—reveals that prompts exhibit distinctive compositional patterns, domain-specific construction variations, and are more instructive and task-oriented than general text. The authors also propose a novel 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. Dataset and code are available for research use at https://anonymous.4open.science/r/LLM-Prompt-Datasets-7416.

LLM Prompt Datasets: In-Depth Analysis and Insights

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

Abstract

Prompts are natural language instructions that define specific tasks for large language models (LLMs) and serve as the primary interface for human–machine interaction. With the wide deployment of LLMs, prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a broad range of applications and content types, enabling broader LLM usage and improved prompt engineering.

Data Collection

The authors compiled a comprehensive collection of 1.22 TB of data containing 673M+ prompt instances from 129 heterogeneous sources:

Dataset platforms
Academic publications
Public repositories
Social media

Taxonomy

A hierarchical taxonomy of LLM prompt datasets is proposed, organized along five facets:

Downstream task
Language
Engineering technique
Attributes
Modality

Analysis Methodology

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

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

Key Findings

Prompts exhibit distinctive compositional patterns compared with other text corpora.
Domain-specific variations exist in prompt construction across applications.
Unique linguistic characteristics distinguish prompts from literary and web content.
Prompts tend to be more instructive and task-oriented than general text.

Prompt Optimization Method

A novel prompt optimization approach leveraging syntactic embeddings:

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

This improves the 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 optimization
Facilitates broader LLM deployment across applications

Resources

The dataset and code are available for research use:

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

Over 1.22 TB of curated prompt data for research purposes.

LLM Prompt Datasets: In-Depth Analysis and Insights

Summary

LLM Prompt Datasets: In-Depth Analysis and Insights

Abstract

Data Collection

Taxonomy

Analysis Methodology

Key Findings

Prompt Optimization Method

Impact and Applications

Resources

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