| --- |
| license: apache-2.0 |
| task_categories: |
| - text-classification |
| - text-generation |
| language: |
| - en |
| tags: |
| - ai-generated-text-detection |
| - adversarial-robustness |
| - paraphrase-attacks |
| - stealthrl |
| - benchmark |
| pretty_name: StealthRL Benchmark |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.parquet |
| --- |
| |
| # StealthRL Benchmark |
|
|
| A paired benchmark for evaluating AI-text detectors under adversarial rewriting. |
| Each row contains an AI-generated source text and a StealthRL rewrite. |
|
|
| Links: |
|
|
| - Paper: https://arxiv.org/abs/2602.08934 |
| - Model: https://huggingface.co/suraj-ranganath/StealthRL |
| - Demo: https://stealthrl.pages.dev/ |
| - Source dataset: https://huggingface.co/datasets/yaful/MAGE |
|
|
| ## Dataset construction |
|
|
| This benchmark uses the same filtered MAGE test-pool AI examples used in the |
| StealthRL paper. We start from the MAGE test split, apply the paper's evaluation |
| filtering, and keep the **14,656 AI-generated examples** used for the full |
| detector evaluation. |
|
|
| For each source example, we include the original AI-generated text and a |
| StealthRL rewrite generated directly with the released StealthRL model. |
|
|
| ## Columns |
|
|
| - `sample_id`: stable row identifier from the filtered MAGE test pool. |
| - `source`: raw MAGE source field. |
| - `source_domain`: parsed source/domain family, e.g. `imdb`, `xsum`, `squad`, `yelp`, `cmv`. |
| - `source_generator`: parsed original generator when available, e.g. `gpt4`, `text-davinci-003`, `gpt-3.5-trubo`. |
| - `source_task`: parsed generation mode when available, e.g. `continuation`, `specified`, `topical`, `paraphrase`, `direct`. |
| - `ai_generated_text`: original AI-generated text evaluated in the paper. |
| - `stealthrl_text`: StealthRL-model rewrite of the AI-generated text. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("suraj-ranganath/StealthRL-Benchmark")["train"] |
| ``` |
|
|
| Use `ai_generated_text` to measure clean AI-text detection and `stealthrl_text` |
| to measure detector robustness to StealthRL adversarial paraphrases. |
|
|
| ## Provenance |
|
|
| The source examples come from MAGE (`yaful/MAGE`, Apache-2.0). The subset |
| corresponds to the filtered full-MAGE evaluation in the StealthRL paper. |
|
|
