Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Languages:
English
Size:
100K - 1M
Tags:
ai-generated-text
machine-generated-text-detection
authorship-attribution
model-attribution
distribution-shift
llm
License:
| language: | |
| - en | |
| license: cc-by-nc-4.0 | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - multi-class-classification | |
| annotations_creators: | |
| - machine-generated | |
| language_creators: | |
| - machine-generated | |
| multilinguality: | |
| - monolingual | |
| source_datasets: | |
| - original | |
| tags: | |
| - ai-generated-text | |
| - machine-generated-text-detection | |
| - authorship-attribution | |
| - model-attribution | |
| - distribution-shift | |
| - llm | |
| pretty_name: MAABench | |
| size_categories: | |
| - 100K<n<1M | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: id | |
| path: maabench_id.jsonl | |
| - split: domain | |
| path: maabench_domain.jsonl | |
| - split: version | |
| path: maabench_version.jsonl | |
| - split: joint | |
| path: maabench_joint.jsonl | |
| # MAABench | |
| MAABench is a benchmark for **machine-text attribution**: given a document written by an LLM, | |
| predict which model family wrote it. There are five families: `gpt`, `claude`, `gemini`, | |
| `mistral` and `llama`. | |
| Besides an in-distribution split, it tests three kinds of distribution shift: unseen domains, | |
| newer model versions, and both at once. | |
| ## Files | |
| | file | domains | model versions | rows | | |
| |---|---|---|---| | |
| | `maabench_id.jsonl` | academic, comment, news, wiki | training versions | 169,735 | | |
| | `maabench_domain.jsonl` | patent, stackexchange | training versions | 7,470 | | |
| | `maabench_version.jsonl` | academic, comment, news, wiki | 3 newer versions per family | 29,700 | | |
| | `maabench_joint.jsonl` | patent, stackexchange | 3 newer versions per family | 21,615 | | |
| `maabench_id.jsonl` holds 29,952 train, 1,998 val and 1,997 test rows per family. Every family | |
| has the same number of rows in every file. | |
| ## Usage | |
| ```python | |
| from datasets import load_dataset | |
| # one split | |
| id_data = load_dataset("ming9999/maabench", split="id") | |
| train = id_data.filter(lambda r: r["split"] == "train") | |
| test = id_data.filter(lambda r: r["split"] == "test") | |
| # all four files | |
| maabench = load_dataset("ming9999/maabench") | |
| version_shift = maabench["version"] | |
| newest = version_shift.filter(lambda r: r["role"] == "test-3") | |
| ``` | |
| The label is `source`. Train on the `train` rows of the `id` split, tune on `val`, and report | |
| accuracy on the `test` rows of `id` and on the `domain`, `version` and `joint` splits. | |
| ## Statistics | |
| Rows per domain (each count is split evenly over the five families): | |
| | file | academic | comment | news | wiki | patent | stackexchange | total | | |
| |---|---|---|---|---|---|---|---| | |
| | `id` (train) | 37,485 | 37,500 | 37,420 | 37,355 | – | – | 149,760 | | |
| | `id` (val) | 2,495 | 2,500 | 2,500 | 2,495 | – | – | 9,990 | | |
| | `id` (test) | 2,500 | 2,500 | 2,495 | 2,490 | – | – | 9,985 | | |
| | `domain` | – | – | – | – | 3,725 | 3,745 | 7,470 | | |
| | `version` | 7,485 | 7,500 | 7,455 | 7,260 | – | – | 29,700 | | |
| | `joint` | – | – | – | – | 11,100 | 10,515 | 21,615 | | |
| In `version` and `joint`, each of `test-1`, `test-2` and `test-3` has a third of the rows | |
| (9,900 and 7,205 rows respectively). | |
| Seed documents: | |
| | file | unique seeds | rows per seed | | |
| |---|---|---| | |
| | `id` | 33,947 (29,952 train / 1,998 val / 1,997 test) | 5 (one per family) | | |
| | `domain` | 1,494 | 5 | | |
| | `version` | 1,980 | 15 (5 families x 3 versions) | | |
| | `joint` | 1,441 | 15 | | |
| Train, val and test in `id` share no seeds. Every `version` seed is an `id` test seed, so | |
| `version` rows can be compared with the `id` test rows written from the same seed by the training | |
| version. 1,439 of the 1,441 `joint` seeds are also `domain` seeds. | |
| Text length in whitespace-separated words: | |
| | file | min | median | mean | max | | |
| |---|---|---|---|---| | |
| | `id` | 81 | 191 | 184.8 | 242 | | |
| | `domain` | 82 | 189 | 184.2 | 242 | | |
| | `version` | 81 | 186 | 183.7 | 242 | | |
| | `joint` | 82 | 188 | 185.6 | 242 | | |
| ## Model versions | |
| | family | training version | newer versions (test-1 / test-2 / test-3) | | |
| |---|---|---| | |
| | gpt | gpt-5 | gpt-5.2 / gpt-5.4 / gpt-5.6-terra | | |
| | claude | claude-opus-4.6 | claude-opus-4.7 / claude-opus-4.8 / claude-opus-5 | | |
| | gemini | gemini-2.5-flash | gemini-3-flash-preview / gemini-3.5-flash / gemini-3.6-flash | | |
| | mistral | Mistral-Small-24B-Instruct-2501 | Mistral-Small-3.1 (2503) / Mistral-Small-3.2 (2506) / mistral-small-2603 | | |
| | llama | Meta-Llama-3-70B-Instruct | Llama-3.1-70B-Instruct / Llama-3.3-70B-Instruct / Llama-4-Scout-17B | | |
| ## Fields | |
| | field | meaning | | |
| |---|---| | |
| | `id` | row id: `<seed_id>:<source>:<role>` | | |
| | `text` | the generated document | | |
| | `source` | label: `gpt`, `claude`, `gemini`, `mistral` or `llama` | | |
| | `model` | the exact model that generated the text | | |
| | `role` | `train` for the training version, `test-1/2/3` for the newer versions | | |
| | `domain` | `academic`, `comment`, `news`, `wiki`, `patent` or `stackexchange` | | |
| | `split` | `train`, `val` or `test` in `maabench_id`; `test` elsewhere | | |
| | `seed_id` | the human-written document the prompt was built from (domain + hash of its text) | | |
| ## Human-written text | |
| Only machine-written rows are included. Each prompt was built from a human-written document from | |
| a public corpus, and those documents are not redistributed here. The code release can rebuild | |
| them from the original corpora and checks every rebuilt text against its `seed_id` hash. | |
| ## Intended use and limitations | |
| MAABench is meant for research on attributing machine-written text to the model family that | |
| wrote it, and on how attribution holds up under domain and model-version shift. | |
| - **Not for decisions about people.** Attribution accuracy on this benchmark does not show that a | |
| detector is reliable on real-world text. Do not use models trained or tuned on MAABench as the | |
| sole basis for judging whether a person used an LLM, e.g. in academic-integrity, hiring or | |
| content-moderation decisions. | |
| - **Closed label set.** Every text comes from one of five families. Text from other families, | |
| human-written text, and human-edited or mixed text are not covered. | |
| - **One training model per family.** Each family is represented in training by a single model, | |
| so results reflect that model's style as much as the family's. | |
| - **Fixed format.** All texts are English and roughly 80-240 words long, generated from prompts | |
| seeded by a human-written document. Shorter, longer, multilingual or freely prompted text may | |
| behave differently. | |
| - **Snapshot in time.** API models change behind the same name; the `model` field records the | |
| identifier used at generation time. | |
| - **Generated content.** Texts were not fact-checked and may contain invented names, numbers | |
| and claims. Do not treat them as factual. | |
| - **Model terms.** The texts are outputs of commercial and open-weight models. Users are | |
| responsible for complying with the respective providers' terms when using them. | |