| --- |
| pretty_name: WildOutlines |
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| task_categories: |
| - text-classification |
| tags: |
| - ai-text-detection |
| - idea-provenance |
| - outlines |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - split: validation |
| path: data/validation-*.parquet |
| - split: calibration |
| path: data/calibration-*.parquet |
| - split: test |
| path: data/test-*.parquet |
| --- |
| |
| # WildOutlines |
|
|
| WildOutlines is the training corpus of [IdeaLens](https://huggingface.co/rishanthrajendhran/IdeaLens), a detector of |
| **who came up with the ideas** in a document, rather than who wrote its words. It pairs 1,001,145 English web |
| documents with **role-labelled outlines** of their ideas: an ordered list of items, each giving one idea and the |
| role it plays in the document (for example *Central Development*, *Source Viewpoint*, *Open Question*). |
|
|
| Every document carries a human or AI label. Each outline comes in two versions: the outline as extracted, which is |
| what IdeaLens reads when scoring a new document, and a paraphrased outline with the document's wording removed, |
| which is what IdeaLens was trained on. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| train = load_dataset("rishanthrajendhran/WildOutlines", split="train") |
| row = train[0] |
| print(row["label"], row["format"]) |
| for item in row["paraphrased_outline"]["items"]: |
| print(f"[{item['role']}] {item['content']}") |
| ``` |
|
|
| IdeaLens reads an outline as one line per item, `[role] content`, joined with newlines. |
|
|
| ## Splits |
|
|
| | Split | Documents | Human | AI | Purpose | |
| |---|---:|---:|---:|---| |
| | `train` | 842,301 | 370,882 | 471,419 | model training | |
| | `validation` | 29,975 | 24,985 | 4,990 | checkpoint selection | |
| | `calibration` | 80,000 | 80,000 | 0 | fitting decision thresholds | |
| | `test` | 48,869 | 24,574 | 24,295 | held-out evaluation | |
|
|
| `calibration` contains only human documents, 10,000 per format, and is disjoint from `validation`. The IdeaLens |
| decision thresholds are the score levels that flag 1% of these documents, either over all of them (global cut) or |
| within each format (per-format cut). `validation` is mostly human, so report AUC or balanced metrics on it rather |
| than accuracy. `test` is close to balanced within every format. |
|
|
| ## Fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | string | Document id: the FineWeb record id (`<urn:uuid:...>`) for the 903,470 pages from the 2024 and 2025 crawls, a 64-character hash for the 97,675 pages from the 2026 crawls | |
| | `text` | string | The full document | |
| | `label` | string | `human` or `ai` (see *Labels*) | |
| | `format` | string | WebOrganizer format, one of eight: Nonfiction Writing, Knowledge Article, Personal Blog, News Article, Academic Writing, User Reviews, Personal About Page, Creative Writing | |
| | `topic` | string | WebOrganizer topic (23 values) | |
| | `url` | string | Source URL | |
| | `date` | string | Crawl timestamp (ISO 8601) | |
| | `word_count` | int | Words in `text` (501 to 17,809) | |
| | `outline` | struct | The outline as extracted from the document | |
| | `paraphrased_outline` | struct | The same outline, paraphrased to remove the document's wording | |
| | `extractor` | string | Model that extracted the outline: `gemini-3.7-flash` or `gemini-3.1-pro` | |
| | `paraphraser` | string | Model that paraphrased it: `gemini-3.1-pro` | |
| | `metadata` | struct | Detector outputs available for the document (see *Metadata*) | |
|
|
| Both outline fields share one structure: |
|
|
| ``` |
| document_description: string # what kind of document this is: genre, audience, register |
| global_themes: list<string> # the document's main points |
| items: list<{ |
| role: string, # the role this item plays, from the format's role vocabulary |
| content: string, # one idea, stated in one or two sentences |
| verbatim: bool # true if the content quotes the document rather than summarising it |
| }> |
| ``` |
|
|
| The paraphrase keeps every item's role, order and content, and changes only the wording. |
|
|
| ## How the corpus was built |
|
|
| - **Documents and labels.** Documents are English Common Crawl pages from the 2024, 2025 and 2026 crawls, drawn from |
| the [WildAI](https://github.com/pangramlabs/WildAI) corpus ([Russell et al., 2026](https://arxiv.org/abs/2609.40295)), |
| which builds on FineWeb and annotates each page with a WebOrganizer format and topic and a |
| Pangram authorship prediction. We kept documents of at least 500 words, in eight long-form formats, that Pangram |
| labels as entirely human-written or entirely AI-generated. Documents Pangram labels as mixed or AI-assisted were left |
| out. The paper describes the construction in full. |
| - **Outline extraction.** For each format, a role vocabulary lists the recurring functions an idea can serve in that |
| kind of document. An LLM, given the document, its format's role vocabulary and six worked examples, writes the |
| outline. Most documents were extracted by `gemini-3.7-flash`; part of the corpus, from an earlier build, by |
| `gemini-3.1-pro`. The `extractor` field records which. |
| - **Paraphrasing.** `gemini-3.1-pro` rewrote every outline item without seeing the source document, so that the |
| document's own wording does not carry into training. |
|
|
| The extraction prompts, role vocabularies and worked examples ship with the [idealens](https://github.com/RishanthRajendhran/IdeaLens) package |
| ([PyPI](https://pypi.org/project/idealens/)), which runs the same extraction on new documents and scores them with the released detectors. |
|
|
| ## Labels |
|
|
| The labels come from the data source, which assigned them with the Pangram AI-text detector: Pangram 3.3.2 for pages |
| from the 2024 and 2025 crawls, and Pangram 4 for pages from the 2026 crawls (`metadata.pangram.model` records which). |
| They are **silver labels**: they record whether Pangram judged the prose to be human-written or AI-generated, not an |
| annotation of who conceived the ideas. IdeaLens is trained on these prose labels through outlines whose wording has |
| been removed, which pushes it to learn from the ideas themselves. |
|
|
| ## Metadata |
|
|
| `metadata` holds the detector outputs that exist for a document. Each block is either complete or null: |
|
|
| ``` |
| metadata: |
| pangram: # every document |
| prediction: "Human" | "AI" # the verdict the label was taken from |
| model: "pangram-3" | "pangram-4" # the Pangram model behind the label |
| version: string | null # "4.0" on the 10,000 documents we scored with Pangram 4 ourselves |
| score_confidence: {score, confidence} | null # 603,132 documents |
| fractions: {ai, ai_assisted, human} | null # 97,675 documents (2026 crawls): share of the text in each class |
| editlens: {score, bucket} | null # 57,886 documents (2026 crawls), EditLens AI-editing score |
| ``` |
|
|
| | Documents | Crawls | `model` | Pangram outputs present | |
| |---:|---|---|---| |
| | 603,132 | 2024, 2025 | `pangram-3` | prediction, score and confidence | |
| | 300,338 | 2024, 2025 | `pangram-3` | prediction only | |
| | 97,675 | 2026 | `pangram-4` | prediction and fractions (EditLens score for 57,886) | |
|
|
| ## Intended use |
|
|
| Training and evaluating detectors of idea provenance, and studying how human and AI-generated documents differ in |
| their ideas and structure. A detector trained on this corpus estimates the provenance of a document's ideas; it |
| should not be the sole basis for decisions about a person's work. |
|
|
| ## Limitations |
|
|
| - English only; documents of at least 500 words; eight long-form web formats. |
| - Labels are detector-derived (see *Labels*). |
| - Two extraction models were used; detectors trained here are expected to be robust to the extractor, and the paper |
| tests this. |
| - Creative Writing and Personal About Page lean human (few AI-labelled documents were available in these formats). |
| - Outlines were written by LLMs and can contain extraction errors. |
|
|
| ## Related |
|
|
| All IdeaLens models and datasets are in the [IdeaLens collection](https://huggingface.co/collections/rishanthrajendhran/idealens-6abee785ce6196fc0be9200f). The [idealens](https://github.com/RishanthRajendhran/IdeaLens) package runs them. |
|
|
| ## License |
|
|
| The dataset (outlines, paraphrases, labels and metadata, and its compilation) is released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/): free to share |
| and adapt for non-commercial purposes, with attribution and under the same license. The document text comes from |
| FineWeb (ODC-By 1.0) via WildAI and remains subject to the terms of its original sources and of Common Crawl. The |
| outlines were generated with Google Gemini models. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{idealens2026, |
| title = {IdeaLens: Detecting AI Ideas in Long-form Writing}, |
| author = {Rajendhran, Rishanth and Choi, Minjoon and Russell, Jenna and Namuduri, Ramya and B{\"o}l{\"o}ni-Turgut, Deniz and Karpinska, Marzena and Wieting, John and Iyyer, Mohit}, |
| journal = {arXiv preprint arXiv:2610.06778}, |
| year = {2026}, |
| eprint = {2610.06778}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL}, |
| url = {https://arxiv.org/abs/2610.06778} |
| } |
| ``` |
|
|
