---
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](https://huggingface.co/datasets/allenai/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:

```json
{"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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/allenai/WildChat)
- Intermediate: [xlr8harder/wildchat-filtered-prompts](https://huggingface.co/datasets/xlr8harder/wildchat-filtered-prompts)

## License

[ODC-BY](https://opendatacommons.org/licenses/by/1-0/) (inherited from WildChat)

## Citation

```bibtex
@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}
}
```