---
license: cc-by-nc-sa-4.0
language:
- am
- ar
- bn
- de
- es
- fa
- fr
- hi
- id
- ja
- km
- ml
- mr
- ne
- ru
- si
- sw
- ta
- th
- uk
- ur
- vi
- yo
- zh
tags:
- ai-text-detection
- idea-provenance
- multilingual
pretty_name: IdeaShift-X
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: source
    path: data/source.parquet
  - split: level_0
    path: data/level_0.parquet
  - split: level_5
    path: data/level_5.parquet
---

# IdeaShift-X

IdeaShift-X tests idea-provenance detection outside English. It holds about 10,000 human-written web pages in 24
languages from [FineWeb2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2). Each page has two AI versions in
the same language, written by Gemini 3.7 Flash:

- **Level 0:** written from a brief naming only the document type, so the ideas are the AI's.
- **Level 5:** written from the page's full role-labelled outline with an instruction to add nothing, so the ideas
  are the page's and the prose is the AI's.

A detector should not flag the human pages. It should flag level 0 for AI ideas. An idea detector should not flag
level 5; a prose detector should.

## Construction

The dataset merges two builds.

- **9-language build:** Arabic, Chinese, German, Indonesian, Japanese, Russian, Spanish, Tamil, Urdu. About 610-740
  pages per language.
- **24-language build:** adds 15 languages at about 250 pages each.

Pages were format-classified with WebOrganizer's annotation prompt on an LLM. Only pages in the eight long-form
formats IdeaLens was trained on were kept, some of them force-fitted to the closest format. Outlines are always
extracted in English, whatever the page's language.

## Splits

| Split | Rows | What it holds |
|---|---:|---|
| `source` | 10,160 | the human-written FineWeb2 pages |
| `level_0` | 10,152 | the AI version written from a brief naming only the document type (AI ideas) |
| `level_5` | 10,116 | the AI version written from the page's full outline (human ideas, AI prose) |

Rows in different splits share `seed_id`; a few pages lack one of their AI versions. `language` selects a language,
e.g. `load_dataset("rishanthrajendhran/IdeaShift-X", split="level_5").filter(lambda r: r["language"] == "fra_Latn")`.

## Fields

| Field | Description |
|---|---|
| `id` | `<language>#<FineWeb2 id>` for human pages; `…\|level0` / `…\|level5` for AI versions |
| `seed_id` | the human page the row belongs to |
| `language`, `language_name` | FineWeb2 language code and name |
| `build` | `9-language` or `24-language` |
| `level` | null for the human page; 0 or 5 for AI versions |
| `kind`, `prose_author`, `idea_author` | what the row is and who wrote its prose and ideas |
| `generator` | `gemini-3.7-flash` for AI versions |
| `format` | the page's format (WebOrganizer taxonomy, one of the eight long-form formats) |
| `words`, `text` | the document |
| `seed_crawl_dump`, `seed_crawl_date` | Common Crawl snapshot and crawl date of the human page |
| `system_prompt`, `prompt` | the exact generation request of a level-0 document (`system_prompt` is always "You are a skilled writer."); empty for the human pages and for level 5 |
| `outline` | the English, role-labelled outline of this document as IdeaLens scored it (JSON string: `document_description`, `global_themes`, `items` of `role` and `content`) |
| `idealens_p_ai`, `proselens_p_ai`, `pangram4_p_ai` | detector P(AI) as reported in the paper (IdeaLens on the outline, ProseLens and Pangram 4 on the document) |

## Caveats

- URLs are not included. The FineWeb2 id identifies each page in the source dataset.
- Outlines are the untreated outlines each document was scored on, not the paraphrased outlines used for training. The
  level-5 prompt is assembled from a template for each document and was not stored, so `system_prompt` and `prompt` are
  empty there.
- Scores are stored at full precision. Rounding them ties the very small P(AI) values and moves the AUC. `pangram4_p_ai`
  is empty for a few documents Pangram returned nothing for.
- Flag a document at the 1% global cut, as in the paper: IdeaLens when `idealens_p_ai` is above 0.86288, Pangram 4 when
  `pangram4_p_ai` is at least 0.5, ProseLens when `proselens_p_ai` is above 0.39461.
- Outlines and page text can contain web addresses or e-mail addresses that were on the original page.

## Scoring

The [idealens](https://github.com/RishanthRajendhran/IdeaLens) package ([PyPI](https://pypi.org/project/idealens/)) runs IdeaLens, ProseLens and the other released detectors on these documents: `pip install idealens`, then `idealens run` on a JSONL file with a `text` field.

All IdeaLens models and datasets are in the [IdeaLens collection](https://huggingface.co/collections/rishanthrajendhran/idealens-6abee785ce6196fc0be9200f).

## License

The dataset 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 human pages come from FineWeb2 (ODC-By 1.0) and remain subject to the terms of their original
sources and of Common Crawl. The AI versions were produced 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}
}
```