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

• URL: http://arxiv.org/abs/2510.09316v1
• Date: Fri, 10 Oct 2025 12:15:55 GMT
• Title: Large Language Model Prompt Datasets: An In-depth Analysis and Insights
• Authors: Yuanming Zhang, Yan Lin, Arijit Khan, Huaiyu Wan,
• Abstract summary: A prompt is a natural language instruction that defines a specific task for a large language model (LLM)<n>In this work, we--for the first time--have compiled an extensive list of prompt datasets sourced from various channels.
• Score: 17.386420251846953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• High Fidelity Textual User Representation over Heterogeneous Sources via Reinforcement Learning [7.091225956788446]
  We propose a novel Reinforcement Learning (RL) framework to synthesize a unified textual representation for each member.<n>Our approach leverages implicit user engagement signals (e.g., clicks, applies) as the primary reward to distill salient information.<n>This work provides a practical, labeling-free, and scalable solution for constructing interpretable user representations.
  arXiv  Detail & Related papers  (2026-02-07T03:27:55Z)
• Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers [51.64398574262054]
  This paper introduces Term IDs (TIDs), defined as a set of semantically rich and standardized textual keywords, to serve as robust item identifiers.<n>We propose GRLM, a novel framework centered on TIDs, to convert item's metadata into standardized TIDs and utilize Integrative Instruction Fine-tuning to collaboratively optimize term internalization and sequential recommendation.
  arXiv  Detail & Related papers  (2026-01-11T07:53:20Z)
• Large Language Models are Good Relational Learners [55.40941576497973]
  We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
  arXiv  Detail & Related papers  (2025-06-06T04:07:55Z)
• SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization [70.11167263638562]
  Social relation reasoning aims to identify relation categories such as friends, spouses, and colleagues from images. We first present a simple yet well-crafted framework named name, which combines the perception capability of Vision Foundation Models (VFMs) and the reasoning capability of Large Language Models (LLMs) within a modular framework.
  arXiv  Detail & Related papers  (2024-10-28T18:10:26Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries.<n>We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks.<n> Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models [0.42832989850721054]
  Multimodal Entities Linking (MEL) is a crucial task that aims at linking ambiguous mentions within multimodal contexts to referent entities in a multimodal knowledge base, such as Wikipedia. Existing methods overcomplicate the MEL task and overlook the visual semantic information, which makes them costly and hard to scale. We propose UniMEL, a unified framework which establishes a new paradigm to process multimodal entity linking tasks using Large Language Models.
  arXiv  Detail & Related papers  (2024-07-23T03:58:08Z)
• Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text [44.81698187939784]
  This paper introduces Structure Guided Prompt, a framework designed to improve the multi-step reasoning capabilities of Large Language Models (LLMs) Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.
  arXiv  Detail & Related papers  (2024-02-20T22:56:23Z)
• INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning [59.07490387145391]
  Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. Their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. We introduce a novel instruction tuning dataset, INTERS, encompassing 20 tasks across three fundamental IR categories.
  arXiv  Detail & Related papers  (2024-01-12T12:10:28Z)
• Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning [36.14688633670085]
  We propose MTPrompt, a multi-dimensional task prompt learning method based on task-related object, summary, and task description information. By automatically building and searching for appropriate prompts, our proposed MTPrompt achieves the best results on few-shot samples setting and five different datasets.
  arXiv  Detail & Related papers  (2023-12-13T10:00:44Z)
• AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators [98.11286353828525]
  GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. We propose AnnoLLM, which adopts a two-step approach, explain-then-annotate. We build the first conversation-based information retrieval dataset employing AnnoLLM.
  arXiv  Detail & Related papers  (2023-03-29T17:03:21Z)
• Unified Text Structuralization with Instruction-tuned Language Models [28.869098023025753]
  We propose a simple and efficient approach to instruct large language model (LLM) to extract a variety of structures from texts. Experiments show that this approach can enable language models to perform comparable with other state-of-the-art methods on datasets of a variety of languages and knowledge.
  arXiv  Detail & Related papers  (2023-03-27T07:39:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.