license: odc-by
language:
- en
tags:
- wildchat
- filtered
- prompts
- training-data
- openness-rated
pretty_name: WildChat Filtered Rated Prompts
size_categories:
- 100K<n<1M
WildChat Filtered Rated Prompts
A cleaned subset of the WildChat dataset, filtered for quality and rated for "prompt openness" (how much a prompt invites genuine engagement vs mechanical task completion).
Dataset Description
305,246 unique user prompts with openness ratings (1-5 scale).
| Score | Description | Count |
|---|---|---|
| 5 | Highly open — invites reflection, opinion, dialogue | 40,792 |
| 4 | Mostly open — flexible approach, room for voice | 52,900 |
| 3 | Mixed — could go either way | 15,081 |
| 2 | Mostly closed — limited flexibility | 118,932 |
| 1 | Fully closed — single correct answer | 77,541 |
Format
JSONL with one prompt per line:
{"id": "sha256hash", "prompt": "user prompt text", "score": 4, "analysis": "Brief explanation of rating"}
Pipeline
Stage 1: Initial Filtering (329k → 305k)
From xlr8harder/wildchat-filtered-prompts:
| Stage | Output | Method |
|---|---|---|
| Raw WildChat | 3.1M | Original dataset |
| Extract + dedup | 2.4M | First user message, SHA256 dedup |
| Length + language | 962k | 20-5000 chars, English (langdetect) |
| Fuzzy dedup | 456k | MinHash LSH, Jaccard 0.7 |
| N-gram spam | 351k | Trigram frequency filtering |
| Cluster spam | 329k | TF-IDF + KMeans tight clusters |
Stage 2: Openness Rating (329k)
Each prompt rated 1-5 for "openness" using LLM classification with the following rubric:
- 5 (Highly Open): Invites reflection, opinion, creative exploration, genuine dialogue
- 4 (Mostly Open): Room for interpretation, flexible approach, voice matters
- 3 (Mixed): Could be answered mechanically or with engagement
- 2 (Mostly Closed): Specific task, limited flexibility, mostly one right approach
- 1 (Fully Closed): Single correct answer, pure lookup/computation
Stage 3: Additional Cleaning (329k → 305k)
| Filter | Removed | Method |
|---|---|---|
| Non-English | 15,649 (4.8%) | 10+ non-Latin Unicode characters |
| Template spam | 8,368 (2.5%) | Prefix-based dedup, cap at 2 per template |
Non-English removal: Prompts with significant non-Latin script (Cyrillic, Chinese, Arabic, Korean, Japanese, Hebrew, Thai, Devanagari, Tamil, Telugu, Bengali). Note: langdetect in Stage 1 caught most non-English, but some mixed-language prompts remained.
Template spam detection: Identified 339 template patterns (prompts sharing the same 50-char prefix, appearing 10+ times). Each template capped at 2 examples for diversity. Top templates removed:
- "Write dialogue from a scene from the animated teen series Jane..." (268 → 2)
- "generate a description with keywords and SEOs for..." (249 → 2)
- "Create a scam-baiting response to the following email..." (185 → 2)
- "Create a hypothetical fact sheet/history/commentary..." (~900 → 6)
- Various Russian/French/Vietnamese templates caught by non-English filter
Intended Use
- Training data for language models (diverse prompt types)
- Research on user prompts and interaction patterns
- Studying prompt diversity and openness characteristics
Limitations
- English only (non-English filtered out)
- First turn only (multi-turn context not preserved)
- Openness ratings are LLM-generated, not human-verified
- Template detection may have false positives/negatives
Source
- Original: allenai/WildChat
- Intermediate: xlr8harder/wildchat-filtered-prompts
License
ODC-BY (inherited from WildChat)
Citation
@article{zhao2024wildchat,
title={WildChat: 1M ChatGPT Interaction Logs in the Wild},
author={Zhao, Wenting and Ren, Xiang and Hessel, Jack and Cardie, Claire and Choi, Yejin and Deng, Yuntian},
journal={arXiv preprint arXiv:2405.01470},
year={2024}
}