Datasets › RoFT
RoFT (Real or Fake Text)
RoFT is a dataset of 21,000 human annotations of generated text. The task is "Boundary detection" i.e. given a passage that starts off as human written, determine when the text transitions to being machine generated. The dataset also includes error annotations using the taxonomy introduced in the paper. The data can be used to train automatic detection systems, train automatic error correction, analyze visibility of model errors, and compare performance across models. Data was collected using http://roft.io.
Models: GPT2, GPT2-XL, CTRL, GPT3 "Davinci"
Genres: News, Stories, Recipes, Speeches
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Boundary Detection | RoFT | GigaCheck (DN-DAB-DETR) Accuracy (%) 64.63 | GigaCheck: Detecting LLM-generated Content | — | 4 | Compare |
Papers archive 2025-07-28
2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 3. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| GigaCheck: Detecting LLM-generated Content | 0 | 1 | 31 Oct 2024 | not harvested |
| AI-generated text boundary detection with RoFT | 1 | 3 | 14 Nov 2023 | ran 1 of 1 samples (0 unverified; 1 pointer-only for licence) |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
MIT
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- RoFT
1 variant name, as the archive lists them.
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