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

TL;DR

This work tackles the fragmented landscape of large language model prompt datasets by compiling 129 datasets totaling over 1.22 TB and 673M prompts, and by introducing a hierarchical taxonomy to organize sources, content, methods, and applications. It conducts a comprehensive lexical, syntactic, and semantic analysis on seven representative datasets, revealing domain-specific linguistic patterns and cross-dataset variations. A novel centroid-based prompt optimization method uses POS and dependency embeddings to guide prompts toward high-performing syntactic patterns, demonstrated via case studies that show improved output meaningfulness. By releasing datasets and code, the paper provides a foundation for reproducible analysis and advances in prompt engineering, with future work suggested on adaptive quality assessment and prompt marketplaces.

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. In this work, we--for the first time--have compiled an extensive list of prompt datasets sourced from various channels, representing a spectrum of downstream tasks, languages, engineering techniques, attributes, and modalities. We select key representative datasets for systematic analysis, revealing commonalities and differences in prompt construction across categories, distinguishing them from other text corpora like literature and web. We further propose a prompt optimization approach that leverages syntactic embeddings of part-of-speech and dependency structures. By identifying a centroid representation of prompts and guiding LLMs to rewrite prompts toward this centroid, our method improves the meaningfulness of model outputs. We have made our datasets and code available.

Figures (9)

• Figure 1: The hierarchical taxonomy of prompt datasets
• Figure 2: Comparison of 3/4/5-grams in the same dataset and 5-grams across multiple datasets. The ratio is defined as the count of the specific $n$-gram divided by the count of prompts in the dataset. More comprehensive comparison data and analysis can be found in Appendix \ref{['sec:token-app']}.
• Figure 3: (a-b): The top-10 most common verbs and their top-5 direct noun objects in two prompt datasets. Data for other 5 datasets are shown in Figure \ref{['fig:verb-noun-app']}. (c): Cosine similarity between dataset-level TF-IDF vectors.
• Figure 4: Semantic prompt embeddings distribution.
• Figure 5: A case study of prompt optimization