| # Data sources |
|
|
| `prompts.txt`, `replies.txt` and `wildchat.txt` are produced by `scripts/fetch_coding_prompts.py`. |
| Every line is English prose typed by people, taken from the datasets below. LLM-generated |
| messages are excluded: the OASST `synthetic` flag, and WildChat's assistant turns, which are |
| never used. The script keeps only prose: it removes code blocks, code-looking |
| lines and markdown, keeps up to 2 sentences per prompt and 5 per reply, and keeps sentences |
| of 3–24 words. |
|
|
| | File | Dataset | What is used | License | Attribution | |
| |---|---|---|---|---| |
| | `prompts.txt` | [OpenAssistant/oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2) | English **user** messages (all turns, reviewed, not deleted, not synthetic) from conversations whose opening prompt is about code | Apache-2.0 | Köpf et al., *OpenAssistant Conversations: Democratizing Large Language Model Alignment*, 2023 | |
| | `prompts.txt` | [google-research-datasets/mbpp](https://huggingface.co/datasets/google-research-datasets/mbpp) | task descriptions (`text`), all splits | CC-BY-4.0 | Austin et al., *Program Synthesis with Large Language Models*, 2021 | |
| | `prompts.txt` | [neulab/conala](https://huggingface.co/datasets/neulab/conala) | curated intents (`intent`), paired train + test | MIT | Yin et al., *Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow*, MSR 2018 | |
| | `replies.txt` | [OpenAssistant/oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2) | English **assistant** messages, written by human volunteers, from the same conversations | Apache-2.0 | as above | |
| | `wildchat.txt` | [allenai/WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M) | English **user** turns from conversations whose first user turn is about code. Conversations flagged toxic and turns with redacted PII are skipped. | ODC-BY | Zhao et al., *WildChat: 1M ChatGPT Interaction Logs in the Wild*, ICLR 2024 | |
|
|
| Counts from the last fetch: |
|
|
| | Source | Sentences | |
| |---|---| |
| | OASST user turns | 4,169 | |
| | MBPP | 938 | |
| | CoNaLa | 2,820 | |
| | OASST assistant turns | 13,183 | |
| | WildChat user turns | 127,490 | |
|
|
| After removing duplicates and credential-like sentences: **7,132** lines in `prompts.txt`, |
| **12,840** in `replies.txt` and **105,373** in `wildchat.txt`. |
|
|
| **Credentials:** some users paste API keys and tokens into prompts. Before filtering, the |
| WildChat text contained 3 OpenAI keys, 4 Google API keys and 2 Telegram bot tokens. Every |
| sentence matching a known key format, or containing a 32+ character token that mixes letters |
| and digits, is dropped. That removed 239 lines, mostly tracebacks and file paths. `test_model.py` |
| checks that no data line or vocabulary entry matches. |
|
|
| **WildChat caveat:** the user turns are what people typed into ChatGPT. Some users paste text |
| (including earlier model output) into their messages, so "typed by people" is not perfectly |
| clean. Only patterns that repeat across many prompts survive pruning, and a suggestion is shown |
| only above a confidence threshold, which limits how much any single pasted message can |
| influence what ghost-typer suggests. |
|
|
| The weights contain only n-gram statistics derived from these lines, plus the hand-written |
| phrases in `../phrases/`. No dataset was used to train anything. 10% of `prompts.txt` (chosen |
| by content hash) is held out when measuring precision and keystrokes saved |
| (`scripts/sweep.py`, `scripts/benchmark.py`). |
|
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