LLM Prompt Datasets: An In-Depth Analysis and Insights

Forum topic · ✨步子哥 · 2025-12-11

Summary

Researchers from Beijing Jiaotong University, Aalborg University, and Bowling Green State University present the first comprehensive compilation of prompt datasets for large language models (LLMs). The team collected over 1.22 TB of data containing 673M+ prompt instances from 129 heterogeneous sources, including dataset platforms, academic publications, public repositories, and social media. They propose a hierarchical taxonomy of prompt datasets across downstream tasks, language, engineering techniques, attributes, and modalities. Multi-level linguistic analysis of seven representative datasets at lexical, syntactic, and semantic levels reveals that prompts exhibit distinctive compositional patterns, domain-specific construction variations, and are more instructive and task-oriented than general text. The authors also introduce a novel prompt optimization method using syntactic embeddings—extracting POS and dependency features, identifying centroid representations, and guiding LLM rewriting—which improves output meaningfulness and quality. Dataset and code are available for research use.

LLM Prompt Datasets: An In-Depth Analysis and Insights

Authors: Yuanming Zhang*, Yan Lin*, Aritra Khan†, Huaiyu Wan Affiliations: Beijing Jiaotong University, Aalborg University, Bowling Green State University Date: 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 widespread deployment of LLMs, various prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a broad range of applications and content types, facilitating broader LLM usage and improved prompt engineering.

Data Collection

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 organized by:

Downstream task
Language
Engineering technique
Attributes
Modality

Analysis Methods

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

Lexical level: token distributions, vocabulary analysis
Syntactic level: dependency parsing, POS tagging, TF-IDF
Semantic level: topic modeling, semantic similarity

Key Findings

Prompts exhibit distinctive compositional patterns compared with other text corpora
Domain-specific variations 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

Optimization Method

A novel prompt optimization approach leveraging syntactic embeddings:

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

This improves the 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 optimization
Facilitates broader LLM deployment across applications

Resources

The datasets 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: An In-Depth Analysis and Insights

Summary

LLM Prompt Datasets: An In-Depth Analysis and Insights

Abstract

Data Collection

Taxonomy

Analysis Methods

Key Findings

Optimization Method

Impact and Applications

Resources

Tags