datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
prompt-quality-vs-response-compliance
Prompt Quality vs Response Compliance — per-item results
Per-item numeric results from a study that measures prompt quality and response
compliance as separate constructs, rather than treating a model's response
score as a proxy for the person's prompting skill.
A four-agent evaluator on a locally hosted Qwen 2.5-7B-Instruct judge scores each
prompt as an artifact before any response exists, then scores the resulting
response twice on identical items: once against criteria… See the full description on the dataset page: https://huggingface.co/datasets/shahoismael/prompt-quality-vs-response-compliance.Qwen3-4B_Prompt_Response_Benchmark
🏆 Qwen3-4B Prompt Response Benchmark
This benchmarking dataset contains 10 diverse data points where the Qwen3-4B model makes reasoning and cultural errors in English and Bengali.
🤖 Model: Qwen3-4B
Base model with reasoning capabilities. One of the well known open source Large Language Models.
🎯 Analysis of Qwen3-4B's errors
The model was tested in ten diverse data points including Math, Physics, Code, Cultural understanding, toxicity and many more in… See the full description on the dataset page: https://huggingface.co/datasets/Rabius/Qwen3-4B_Prompt_Response_Benchmark.prompt-response-llmrouterbenchglobal80_prompt-response-pairs
Geometriqs Global80 Prompt–Response Dataset
Overview
This dataset contains the complete set of prompt–response pairs used in the GenAI Positioning Study: Global80 (November 2025).It captures how three leading generative-AI platforms — OpenAI ChatGPT (GPT-4), Google Gemini, and Perplexity AI — respond to a controlled set of neutral, comparative questions about 80 of the world’s largest companies.
The purpose is to measure model behaviour, not user behaviour: how these… See the full description on the dataset page: https://huggingface.co/datasets/geometriqs/global80_prompt-response-pairs.prompt_response_1K_PIIS_completeuser-prompt-responseTrix-Chatbot-Prompt-Response
Dataset Creation Process
Overview
This dataset was created to train and evaluate a chatbot focused on answering questions about Pooria Roy, his background, projects, and related topics. The goal was to build a dataset grounded in real user behavior while maintaining sufficient diversity and coverage of edge cases.
The final dataset contains 2,105 prompt-response examples, including a small portion of multi-turn conversations.
Data Collection Pipeline… See the full description on the dataset page: https://huggingface.co/datasets/regularpooria/Trix-Chatbot-Prompt-Response.sample-prompt-responses