Human-written text, real prompts people wrote, and the generation methods of published papers, gathered from public datasets into one format. It is the input of anagram-bench, a benchmark for AI-text detectors: the benchmark samples from here, asks a language model to write or edit text, and labels the results.

Everything in this repository allows commercial use. Non-commercial material is in the companion repository CoderBak/anagram-corpus-nc; load both for the full corpus (research use).

This version holds 820,570,578 rows in 85 subsets (one per source part). It is being built source by source; more subsets will follow.

Loading

from datasets import load_dataset
ds = load_dataset("CoderBak/anagram-corpus", "ai_text_not_mine")   # subset = source; splits = the source's own splits

Each subset keeps its source's own splits (train, test, ...). A source with no official split has one split, full. Every row records its source's licence in license.

Subsets

Subset	Record type	Rows	Splits	Licence	Source
`ai_text_not_mine`	documents	95	test 95	CC BY-SA 4.0	AITDNA ('Your AI Text is not Mine'): human-only texts and their edit logs
`ai_text_not_mine_keystrokes`	logs	95	test 95	CC BY-SA 4.0	AITDNA ('Your AI Text is not Mine'): human-only texts and their edit logs
`american_stories`	documents	370,313,913	full 370,313,913	CC BY 4.0	American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers
`arxiv`	documents	3,205,432	full 3,205,432	CC0 1.0	arXiv metadata snapshot (titles and abstracts)
`asap_2`	documents	24,728	test 7,421, train 17,307	CC BY 4.0	ASAP 2.0 (Automated Student Assessment Prize 2.0 corpus)
`caselaw_access_project`	documents	6,920,587	full 6,920,587	CC0 1.0	Caselaw Access Project (Harvard Law School Library)
`casimir`	documents	54,675	test 5,579, train 43,615, validation 5,481	MIT	CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions
`casimir_reviews`	documents	185,991	full 185,991	MIT	CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions
`chatbot_arena_140k`	prompts	135,634	train 135,634	CC BY 4.0	Chatbot Arena human preference votes (arena-human-preference-140k and -55k)
`chatbot_arena_55k`	prompts	57,477	train 57,477	Apache 2.0	Chatbot Arena human preference votes (arena-human-preference-140k and -55k)
`chatgpt_detector_bias`	documents	394	full 394	MIT	GPT Detectors Are Biased Against Non-Native English Writers (human-written data)
`civil_comments`	documents	1,999,514	test 97,320, train 1,804,874, validation 97,320	CC0 1.0	Civil Comments (Jigsaw Unintended Bias in Toxicity Classification)
`cmu_movie_summaries`	documents	42,306	full 42,306	CC BY-SA 3.0	CMU Movie Summary Corpus
`co_storm_wildseek`	prompts	100	full 100	CC BY-SA 4.0	WildSeek (Co-STORM: Into the Unknown Unknowns)
`common_pile_foodista`	documents	72,034	train 72,034	CC BY 3.0	Common Pile v0.1 (raw): Ubuntu IRC, Foodista, Regulations.gov, USGPO, GitHub Archive
`common_pile_github_archive`	documents	30,318,756	train 30,318,756	MIT, Apache 2.0, BSD 3-Clause, CC BY 4.0, BSD 2-Clause, CC0 1.0, The Unlicense, ISC, Boost Software License 1.0, zlib License, WTFPL, MIT No Attribution, BSD Zero Clause, Artistic License 2.0, Vim License, Universal Permissive License 1.0, Educational Community License 2.0, BSD 3-Clause Clear, Microsoft Public License, Academic Free License 3.0, LaTeX Project Public License 1.3c, PostgreSQL License, University of Illinois/NCSA Open Source License, BSD 4-Clause, Mulan Permissive Software License 2.0	Common Pile v0.1 (raw): Ubuntu IRC, Foodista, Regulations.gov, USGPO, GitHub Archive
`common_pile_regulations`	documents	222,980	train 222,980	Public domain (U.S. government work or expired copyright)	Common Pile v0.1 (raw): Ubuntu IRC, Foodista, Regulations.gov, USGPO, GitHub Archive
`common_pile_ubuntu_irc`	documents	329,113	train 329,113	Public domain (U.S. government work or expired copyright)	Common Pile v0.1 (raw): Ubuntu IRC, Foodista, Regulations.gov, USGPO, GitHub Archive
`common_pile_usgpo`	documents	2,732,677	train 2,732,677	Public domain (U.S. government work or expired copyright)	Common Pile v0.1 (raw): Ubuntu IRC, Foodista, Regulations.gov, USGPO, GitHub Archive
`congressional_hearings`	documents	35,696	full 35,696	Public domain (U.S. government work or expired copyright)	Congressional hearings (govinfo CHRG)
`congressional_record`	documents	910,669	full 910,669	Public domain (U.S. government work or expired copyright)	Congressional Record (daily edition, govinfo CREC)
`courtlistener`	documents	10,901,752	full 10,901,752	Public domain (U.S. government work or expired copyright)	CourtListener bulk opinions (Free Law Project), snapshot 2026-09-30
`cuad`	documents	510	test 102, train 408	CC BY 4.0	CUAD: Contract Understanding Atticus Dataset v1
`djinni`	documents	141,897	train 141,897	MIT	Djinni Recruitment Dataset (English job descriptions)
`ebg_stylometry`	documents	802	full 802	BSD 3-Clause	Extended Brennan-Greenstadt and Brennan-Greenstadt adversarial stylometry corpora
`ebg_stylometry_bg`	documents	102	full 102	BSD 3-Clause	Extended Brennan-Greenstadt and Brennan-Greenstadt adversarial stylometry corpora
`edgar_corpus`	documents	218,768	test 21,890, train 174,986, validate 21,892	CC BY 4.0	EDGAR-CORPUS: Billions of Tokens Make The World Go Round
`eli5`	prompts	274,736	test 600, train 272,629, validation 1,507	MIT	ELI5: Long Form Question Answering (KILT release)
`elife_plos_lay_summaries`	documents	32,353	test 1,617, train 29,119, validation 1,617	CC BY 4.0	PLOS and eLife lay summarisation corpora (Making Science Simple)
`europarl`	documents	2,787,578	full 2,787,578	Free reuse with acknowledgement of the source (official publications)	Europarl v7: European Parliament Proceedings Parallel Corpus 1996-2011 (source release)
`govreport`	documents	19,466	test 973, train 17,519, valid 974	CC BY 4.0	GovReport
`hh_rlhf_harmless_base`	prompts	44,849	test 2,312, train 42,537	MIT	Anthropic HH-RLHF (helpfulness and harmlessness preference data, red-team attempts)
`hh_rlhf_helpful_base`	prompts	46,189	test 2,354, train 43,835	MIT	Anthropic HH-RLHF (helpfulness and harmlessness preference data, red-team attempts)
`hh_rlhf_helpful_online`	prompts	23,144	test 1,137, train 22,007	MIT	Anthropic HH-RLHF (helpfulness and harmlessness preference data, red-team attempts)
`hh_rlhf_helpful_rejection_sampled`	prompts	55,170	test 2,749, train 52,421	MIT	Anthropic HH-RLHF (helpfulness and harmlessness preference data, red-team attempts)
`hh_rlhf_red_team_attempts`	prompts	38,961	full 38,961	MIT	Anthropic HH-RLHF (helpfulness and harmlessness preference data, red-team attempts)
`ifbench`	prompts	300	test 300	ODC Attribution 1.0	IFBench: Generalizing Verifiable Instruction Following
`issuebench`	prompts	2,490,576	prompts_full 2,490,576	CC BY 4.0	IssueBench
`issuebench_issues`	prompts	396	issues 396	CC BY 4.0	IssueBench
`issuebench_templates`	prompts	3,916	templates_full 3,916	CC BY 4.0	IssueBench
`iterater_full_doc`	documents	63,262	dev 1,712, test 1,854, train 59,696	CC BY-SA 3.0, Apache 2.0, CC BY 2.5	IteraTeR (document level: IteraTeR-FULL and IteraTeR-HUMAN)
`iterater_human_doc`	documents	1,118	dev 54, test 102, train 962	CC BY 2.5, CC BY-SA 3.0, Apache 2.0	IteraTeR (document level: IteraTeR-FULL and IteraTeR-HUMAN)
`kilt`	documents	5,889,353	full 5,889,353	CC BY-SA 3.0	KILT knowledge source (English Wikipedia, 2019-08-01)
`kiwi`	prompts	234	full 234	CC BY-SA 4.0	KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions
`klicke`	documents	4,992	full 4,992	MIT	KLiCKe: Keystroke Logging in Compositions for Knowledge Evaluation
`klicke_keystrokes`	logs	9,984	full 9,984	MIT	KLiCKe: Keystroke Logging in Compositions for Knowledge Evaluation
`klicke_typing_tests`	logs	69,840	full 69,840	MIT	KLiCKe: Keystroke Logging in Compositions for Knowledge Evaluation
`klicke_vocabulary_test`	logs	4,971	full 4,971	MIT	KLiCKe: Keystroke Logging in Compositions for Knowledge Evaluation
`l1_signals`	documents	2,357	acl_postllm 369, acl_prellm 388, arxiv_set 1,600	CC0 1.0, CC BY 4.0	Can We Still Trace L1 Signals? (academic abstracts labelled with the authors' native language)
`longbench_write`	prompts	120	full 120	Apache 2.0	LongBench-Write (LongWriter)
`longbench_write_ruler`	prompts	48	full 48	Apache 2.0	LongBench-Write (LongWriter)
`masc`	documents	392	full 392	CC BY 3.0	MASC 3.0.0 and the Open American National Corpus (OANC)
`masc_oanc`	documents	8,824	full 8,824	CC BY 3.0	MASC 3.0.0 and the Open American National Corpus (OANC)
`medquad`	documents	16,423	full 16,423	CC BY 4.0	MedQuAD: Medical Question Answering Dataset
`methods`	methods	8,670	full 8,670	Apache 2.0, MIT, CC BY 4.0, Not copyrightable (settings and parameters), BSD 3-Clause, CC BY-SA 4.0, CC BY 3.0, GPL 3.0	anagram-bench generation methods
`million_authors`	documents	60,083,121	full 60,083,121	CC BY-SA 4.0	The Million Authors Corpus (MAC)
`million_authors_av`	documents	2,700,034	dev 270,020, test 540,054, train 1,889,960	CC BY-SA 4.0	The Million Authors Corpus (MAC)
`mt_bench`	prompts	33,000	train 33,000	CC BY 4.0	Chatbot Arena Conversations (MT-Bench and Chatbot Arena paper)
`muldiloc`	documents	1,417	full 1,417	MIT	MulDiLoC-HAP: Multidialectal, Longitudinal Corpus of Human-AI Hybrid Production (human-written essays)
`multieurlex`	documents	1,044,739	test 115,000, train 817,239, validation 112,500	CC BY-SA 4.0	MultiEURLEX: multilingual EU legislation with EUROVOC labels
`newswire`	documents	2,719,607	full 2,719,607	CC BY 4.0	Newswire: A Large-Scale Structured Database of a Century of Historical News
`nirvana`	prompts	71	full 71	CC BY 4.0	NIRVANA: A Comprehensive Dataset for Reproducing How Students Use Generative AI for Essay Writing
`parlamint`	documents	8,073,406	full 8,073,406	CC BY 4.0	ParlaMint 5.0: comparable corpora of parliamentary debates (29 parliaments)
`parliament_undisclosed_llm`	documents	17,410	test 1,742, train 13,927, validation 1,741	CC BY 4.0	Detecting undisclosed LLM-generated content in parliamentary texts (data release)
`peerread`	documents	314	dev 13, test 20, train 281	CC BY 4.0	PeerRead: A Dataset of Peer Reviews (ACL 2017 and CoNLL 2016 sections)
`peerread_papers`	documents	159	dev 8, test 9, train 142	CC BY 4.0	PeerRead: A Dataset of Peer Reviews (ACL 2017 and CoNLL 2016 sections)
`pg19`	documents	28,752	test 100, train 28,602, validation 50	Apache 2.0	PG-19 Language Modelling Benchmark
`prism`	prompts	8,011	full 8,011	CC BY 4.0	The PRISM Alignment Dataset
`project_gutenberg`	documents	77,588	full 77,588	Public domain (U.S. government work or expired copyright)	Project Gutenberg (bulk plain-text feed)
`promptevals`	prompts	2,088	test 419, train 1,252, validation 417	MIT	PromptEvals: A Dataset of Assertions and Guardrails for Custom Production LLM Pipelines
`rcv1_reuter_50_50`	documents	5,000	test 2,500, train 2,500	CC BY 4.0	Reuter_50_50 (RCV1 authorship subset)
`realguardrails_distractors`	prompts	504	full 504	CC BY 4.0, CC BY-SA 4.0	RealGuardrails / SystemCheck: A Closer Look at System Prompt Robustness
`realguardrails_handwritten`	prompts	239	full 239	MIT	RealGuardrails / SystemCheck: A Closer Look at System Prompt Robustness
`realguardrails_prompts`	prompts	3,082	full 3,082	MIT	RealGuardrails / SystemCheck: A Closer Look at System Prompt Robustness
`reddit_tifu`	documents	79,750	full 79,750	MIT	Reddit TIFU
`slopbench`	prompts	112	heldout 38, public 74	MIT	SlopBench: How Well Can We Rank Language Models by Slop?
`stack_exchange`	documents	82,921,221	full 82,921,221	CC BY-SA 3.0, CC BY-SA 4.0, CC BY-SA 2.5	Stack Exchange Data Dump (2024-04-02)
`supreme_court_oral_arguments`	documents	1,809	full 1,809	Public domain (U.S. government work or expired copyright)	U.S. Supreme Court oral argument transcripts (OT2000-OT2026)
`un_general_debates`	documents	8,093	full 8,093	CC0 1.0	UN General Debate Corpus (UNGDC) 1970-2018
`un_parallel`	documents	799,173	full 799,173	Public domain (U.S. government work or expired copyright)	The United Nations Parallel Corpus v1.0
`un_parallel_testsets`	documents	48,000	dev 24,000, test 24,000	Public domain (U.S. government work or expired copyright)	The United Nations Parallel Corpus v1.0
`webis_tldr_17`	documents	3,848,330	full 3,848,330	CC BY 4.0	Webis-TLDR-17
`wikiconv`	documents	210,018,590	full 210,018,590	CC BY-SA 3.0	WikiConv (English)
`wikipedia`	documents	6,487,681	full 6,487,681	CC BY-SA 3.0	English Wikipedia (Wikimedia dump enwiki-20220501)
`wildchat`	prompts	834,358	train 834,358	ODC Attribution 1.0	WildChat-1M

Columns

Every row has: id (/); source (source name (= subset)); split (the source's own split, or full when it has none); license (licence of this row); source_record_id (the record's id in the source release); source_url (link to the original record, if any); meta (the source's other fields, as released (JSON)).

documents: text (the human-written text (normalised: see below)); title (title, if the source has one); prompt (the assignment or question the writer answered, if the source gives one); genre (register of the text, as labelled for this corpus); date (date written or published (ISO; as precise as the source allows)); pre_chatgpt (date before 2022-11-30 (null if unknown or ambiguous)); author_id (author id as released by the source); parent_id (thread / document the text belongs to); language (fastText lid.176 language code of the text); language_score (its probability); n_words (whitespace words); text_hash (sha1 of the lower-cased, whitespace-collapsed text).

logs: parent_id (id of the row this record belongs to); format (format of the record); data (the record exactly as released).

methods: kind (template | instruction | attack | classifier); task (the anagram-bench task that uses it); name (template, list or attack id); text (the template or instruction, exactly as quoted); system (system message of a template); placeholders (slots a template expects); params (settings of an attack (JSON)); verbatim (true if copied word for word from the source); paper (source paper); paper_url (its URL); location (where in the paper or repository).

prompts: prompt (the first user turn); system (system prompt, if any); messages (the conversation as {role, content}; assistant content is null when its licence differs from the row's); n_turns (number of user turns); human_response (a human-written answer to the prompt, if the source has one); category (task category given by the source); model (chatbot that answered (chat logs)); date (date of the conversation (ISO)); pre_chatgpt (date before 2022-11-30); language (fastText lid.176 language code of the first turn); language_score (its probability); n_words (words in the first turn); text_hash (sha1 of the first turn, lower-cased, whitespace collapsed).

How it was built

Official releases only, at a pinned version; every record of the release is kept (no sampling, no quality filtering). Rows whose text is empty after normalisation are left out and counted.
Normalisation (the only change to text) is kept minimal so layout survives: Unicode NFC; Windows/Mac newlines to \n; control characters other than tab and newline removed; spaces at the end of a line removed; blank lines before the first line and blank space after the last removed. Indentation, blank lines, markup, spelling, quotes and dashes are left as released. Method texts are not normalised at all.
Language is detected with fastText lid.176 on the first 2,000 characters; all languages are kept.
pre_chatgpt compares the record's date with 2022-11-30 (ChatGPT's release); text written before it is very unlikely to be AI-generated.
The source's other fields are kept in meta as released.

Source notes

`ai_text_not_mine`

AITDNA ('Your AI Text is not Mine'): human-only texts and their edit logs. Dycke, Sakharova, Daheim & Gurevych (arXiv 2026).

The release holds 362 texts from user studies (one split, 'test'), each in nine configs that share rows and metadata: 'original' (the editor log of the text: every insert, delete and retain event with offset, text, seconds since the first edit, and user), 'document' (the final text with its authorship label) and seven other label views (sentence, token, span, boundary, intent, content, membership). Each participant wrote three texts with an LLM assistant and one with LLM support turned off (metadata human_only = true). Included: the 95 human_only texts (subset ai_text_not_mine, documents) and the edit log of each of them (subset ai_text_not_mine_keystrokes, logs, one row per text). Left out: the 267 texts written with LLM support on (human_only = false; co-written with gpt-5.2, deepseek-chat, qwen2.5:7b, llama4:scout or gemini-3-flash-preview, including the 10 peer reviews) and their logs, because they contain or may contain AI-generated text. The seven label views add nothing for human-only texts (every label is 'User') and are not used. Rows keep the release order; source_record_id = 'aitdna_test_<0-based row index>' (the release has no ids). Documents: text = the 'document' config's text segments joined (one segment, author 'User', for every included text; it equals the replay of the log (up to trailing newlines) for 89 of the 95 and differs by 1-2 characters in 6); genre from metadata.task; author_id = metadata.author (participant pseudonym; links the four texts of a session); prompt empty (the participant guidelines and topic lists are only in the paper's figures, and which of two offered topics a writer chose is not released); meta = the release's metadata (author, human_only, model, temperature, setting, task). date = 2026, an upper bound (paper v1 2026-06-03; per-text dates are not released): the sessions' co-writing used models released in 2024-2025 (up to GPT-5.2 and Gemini 3 Flash, December 2025), so every text was written after 2022-11-30. Logs: data = the text's 'original' config event list as a JSON array whose elements are the released event objects in release order (format 'json'); parent_id = the document row id; meta = event counts by operationType and user, and the release metadata. The logs are editor change events batched about every 2 s, not single keystrokes; all events of the included logs are by 'User' (no LLM request or response events).

Licence: Stated inconsistently: the HF card's YAML says cc-by-sa-4.0, the card body says 'License: gpl-2.0' (the code repo is Apache-2.0, the paper CC BY 4.0). The corpus has no GPL-2.0 licence id; rows carry CC BY-SA 4.0, the licence in the card metadata. Both readings allow commercial use and require attribution and share-alike (copyleft) for adaptations, so the repo is the same (anagram-corpus) either way. The texts are by anonymised study participants (pseudonymous author names).

`ai_text_not_mine_keystrokes`

AITDNA ('Your AI Text is not Mine'): human-only texts and their edit logs. Dycke, Sakharova, Daheim & Gurevych (arXiv 2026).

`american_stories`

American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers. Dell, Carlson, Bryan, Silcock, Arora, Shen, D'Amico-Wong, Le, Querubin & Heldring (NeurIPS 2023 Datasets and Benchmarks).

Every associated article ('full articles' entry) of every scan JSON in all 180 year archives (1770-1964): 370,313,913 articles from 16,887,460 scanned pages, in archive order; nothing sampled or filtered. This is the unit of the release's article-level output (official loader AmericanStories.py, config all_years); source_record_id is the loader's article_id, '_<scan file name without .json>' (unique across the release). Every full article is built from exactly one 'article' content region plus the 'headline' and 'author' regions the release associated with it: text = the release's article field (the article region's OCR text as released, OCR errors and hard line breaks kept), title = headline (the headline regions' OCR joined by blank lines; None when empty), meta.byline = byline (the author regions joined). date = the issue date from the scan file name (the loader's date, YYYY-MM-DD; 1770-08-23 .. 1964-12-31, present for every article); parent_id = the scan id (file name without .json; one scan = one newspaper page); source_url = the Chronicling America page image (scan.raw_data_loc, else scan_url; None for the 1,164,263 articles whose scan record has neither); genre = 'newspaper article (historical, OCR)'; no author ids. meta: year_file and scan_file (archive and member name); the loader's fields article_id, newspaper_name (lccn.title), edition and page (parsed from the file name as the loader does); the full-article fields as released (full_article_id, id, byline, object_ids, bbox, bbox_list); objects = the release's record of each content region of the article (id, class, legibility Legible / Questionable / Illegible / NA, reading_order_id, full_article_id, bbox); and scan_record = the scan-level metadata as released (lccn = the newspaper's Library of Congress record: title, lccn, state, issn, start/end year, language(s), geonames/dbpedia ids, ...; edition; scan = page size and Chronicling America URLs; page_number; id; scan_url and scan_ocr where present). The regions' raw_text is not repeated in objects because the article fields restate it exactly (article = the article region's raw_text, headline and byline = the headline and author regions' raw_text joined by blank lines; checked for every article, true for all). Included although the official loader skips them: the articles of the 161,322 scans whose JSON has no lccn record (scan_record.lccn = null; the newspaper's LCCN is still in the file name). Left out: content regions that belong to no full article, which the release's article-level output does not contain either: 18,536,873 article regions, all with empty raw_text (regions not OCR'd, mostly those the legibility classifier marked Illegible or NA), 7,718,136 headline and 230,677 author regions with text but no associated article (stand-alone headlines and bylines), and 7,725,190 empty headline/author regions. No scan file failed to parse and no article has empty text, so the builder dropped nothing. (Code path not taken: an article_id repeating within a year archive would get the suffix '~dup' and meta.duplicate_article_id = true.)

Licence: CC BY 4.0 (dataset card 'license: cc-by-4.0'; LICENSE file = CC BY 4.0 legal code; README: 'The dataset has a CC-BY 4.0 license'). Attribution required. The scans are public-domain Chronicling America pages (Library of Congress); the paper notes that some syndicated content (e.g. comics, fiction) in the newspapers may still be under copyright. Historical newspapers contain period offensive language and personal details of people named in the news.

`arxiv`

arXiv metadata snapshot (titles and abstracts). Cornell University, arXiv Dataset (Kaggle Cornell-University/arxiv); Clement, Bierbaum, O'Keeffe & Alemi, On the Use of ArXiv as a Dataset (2019).

Every record of the snapshot: 3,205,432 papers, one row each, in file order. Nothing is filtered. text = the abstract with arXiv's line wrapping undone by arXiv's own rule ('Carriage returns will be stripped unless they are followed by leading white spaces', info.arxiv.org/help/prep): a line break followed by an indented or empty line is kept as a line break (blank lines kept, runs of them cut to one), every other line break becomes a space, and the indentation and the line ends' spaces are dropped; otherwise the abstract is as released (LaTeX markup such as $...$ kept). title = the title with its wrapping whitespace collapsed to single spaces. The snapshot holds each paper's current metadata, so title and abstract are those of the latest version. date = the creation date of that latest version (UTC), so pre_chatgpt is true only if the text as released was written before 2022-11-30; the first version's date and every other version date are in meta.versions. meta = the record's other fields as released: submitter, authors, authors_parsed, comments, journal-ref, doi, report-no, categories, license (the licence the authors chose for the paper itself, not for this metadata), versions (every version with its created date), update_date. source_record_id = arXiv id; 80 ids occur more than once (old math-ph ids, different records with different titles and abstracts): all occurrences are kept, the 2nd and later get '#2', '#3', ... source_url = https://arxiv.org/abs/. Full texts are not part of this release.

Licence: CC0 1.0: arXiv dedicates all metadata, including titles and abstracts, to the public domain (info.arxiv.org/help/license: 'A Creative Commons CC0 1.0 Universal Public Domain Dedication will apply to all metadata'); Kaggle lists the dataset as 'CC0: Public Domain'. The papers' own licences (meta.license) cover the full texts, which are not included.

`asap_2`

ASAP 2.0 (Automated Student Assessment Prize 2.0 corpus). Crossley et al., 'A large-scale corpus for assessing source-based writing quality: ASAP 2.0' (Assessing Writing 65, 2025; preprint Zenodo 14781349).

Every essay of both official files: train = every row of ASAP_2_Final_github_train.csv in the training zip (17,307 essays), test = every row of ASAP_2_Final_github_test.csv in the password-protected test zip (7,421; password from the README); 24,728 in all (the README's '24,278' is a digit swap). text = full_text; source_record_id = essay_id (unique across both files); prompt = assignment, the assignment wording the student answered (empty for the 28 essays whose assignment is 'NA' in the release); meta = every other column as released (score, set, pubpriv, assignment, prompt_name, economically_disadvantaged, student_disability_status, ell_status, race_ethnicity, gender, grade_level, essay_word_count, task; 'NA' strings kept), plus source_text_file = the PDF of the prompt's source texts in ASAP_2_source_texts.zip, and persuade_2_essay_id_comp = the list of essay_id_comp of the PERSUADE 2.0 essays with the same text (whitespace-collapsed exact match against both official PERSUADE 2.0 files; 12,873 essays — the README says 12,871 come from PERSUADE 2.0 —, which are also in this corpus as subset persuade_2 under CC BY-NC-SA 4.0). Left out: the source texts (ASAP_2_source_texts.zip, 7 PDFs of the test screens: reading passages, assignment and editing checklist). Five are page screenshots without a text layer, and the passages are third-party articles reprinted in the tests by permission with their own notices ('copyright (c) 2013 by Highlights for Children, Inc. ... Used by permission'; 'Copyright (c) 2004 by Mother Jones ... All Rights Reserved'; Cricket Media: 'Any commercial use or distribution of material without permission is strictly prohibited'), which the repo's CC BY 4.0 statement cannot cover; the file name is kept per essay. The scoring rubric (docx) is not an essay. Dates: the release gives none. For the 12,873 essays also in PERSUADE 2.0 date = 2021, the same upper bound as the persuade_2 subset (Kaggle Feedback Prize 2021); the others have no date (essays from state writing tests; the essay-id prefixes AAAOPP13416, AAATRP14318 and AAAVUP14319 and the years some essays mention suggest 2016, 2018 and 2019 test administrations, an inference, not a published date). No author ids or URLs are released.

Licence: CC BY 4.0 (official README at the pinned commit: 'The data is provided under a Attribution 4.0 International (CC BY 4.0) license'). The 12,873 essays that are also in PERSUADE 2.0 were released there by the same group under CC BY-NC-SA 4.0; ASAP 2.0 releases them under CC BY 4.0. The source-text PDFs carry third-party copyright notices and are not included.

`caselaw_access_project`

Caselaw Access Project (Harvard Law School Library). Caselaw Access Project, Harvard Law School Library Innovation Lab (static.case.law bulk export, 2024).

Every case of every volume zip listed in VolumesMetadata.json (40,622 volumes, 6,920,598 cases), one row per case (json/.json in the zip): 6,920,587 rows; the 11 cases whose casebody has neither head matter nor opinion text are dropped by the builder as empty. Nothing is sampled or filtered. text = the case body as plain text: the head matter (caption, docket number, court, date, counsel and, in volumes that are not redacted, the reporter's syllabus or headnotes) followed by each opinion (majority, concurrences, dissents, ... in the release's order), separated by blank lines; each part is the release's plain-text field (casebody.head_matter, casebody.opinions[].text) with its line breaks (one per paragraph). meta.parts gives each part's kind (head_matter / opinion), opinion type and author, and its [start, end) character offsets in text (offsets of the normalised text, valid for the published column). Cases whose casebody has no opinion (e.g. table dispositions: 'Petition for allowance of appeal denied') have only their head matter. Harvard-scanned volumes are uncorrected OCR (provenance.source 'Harvard'; analysis.ocr_confidence); Fastcase volumes are publisher text. title = name_abbreviation; date = decision_date; source_record_id = '//'; source_url = the case JSON on static.case.law; genre 'U.S. court opinion (case reporter)'. meta holds the case JSON's other fields unchanged: id, name, name_abbreviation, decision_date, docket_number, first/last page and page order, file_name, citations, court, jurisdiction, analysis (word and character counts, OCR confidence, PageRank, simhash, sha256), provenance, last_updated, cites_to (the citation graph), and casebody's judges, parties, attorneys and corrections; plus volume (the volume's VolumesMetadata.json entry) and reporter (its ReportersMetadata.json id, full_name, short_name). Left out: the per-case HTML files (the same casebody with markup), the volume-level metadata JSON files (their content is in meta), and the scan PDFs and ALTO/image tars the site also offers.

Licence: CC0 1.0 for the Caselaw Data (case.law Terms of Use, effective 2024-03-13). Court opinions are public-domain government edicts; head matter written by reporter editors is released under the same CC0 terms.

`casimir`

CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions. Jourdan, Boudin, Hernandez & Dufour (LREC-COLING 2024).

casimir: one row per article version of all three splits (mapping_.jsonl: train 12,488 articles / 43,615 versions, dev 1,561 / 5,481, test 1,597 / 5,579; the split names are those of the HF dataset config: train, validation (= the *dev files), test; the README table swaps the validation and test article counts). The release has no document-level text: article_pairs.jsonl holds, for each pair of consecutive versions, the sentences of both versions aligned one to one (a sentence without a counterpart is paired with an empty side), each with its VILA block type (Title, Section, Paragraph, Abstract, List, ...), section, paragraph and sentence numbers. A version's text is rebuilt from one side of one of its pairs (as in the earlier build): that side's sentences in sentence-pair-index order (rows where the version has no sentence id are skipped), a new block whenever the (type, section, paragraph) of the sentence changes or the sentence is a Title or Section, sentences joined by one space, blocks by a blank line. A version in two pairs (with the previous and the next version) is rebuilt from the side with fewer leaked git word-diff markers ('[-...-]', '{+...+}', a CASIMIR artefact), on a tie from the pair with the previous version; meta.rebuilt_from says which. The sentence text is as released: VILA PDF extraction with the authors' clean-up (bibliography removed; equations, figures and tables replaced by [Equation] / [Figure] / [Table]; page and line numbers and hard line breaks removed), so it keeps extraction noise ('(cid:N)' glyph codes, ligatures, letter-spaced small caps such as 'F AIRNESS'); the paragraph layout is our reconstruction. Every version occurs in at least one of the 36,733 pairs, so every version has a text (54,675 rows). Rows are in mapping order (article by article), versions oldest first. source_record_id = the OpenReview revision id; parent_id = the forum (article) id; title = the version's own title in its metadata record, else the forum title; date = the version's OpenReview tcdate (creation time of that revision, UTC). meta: id_forum, version (1 = oldest), n_versions, split, rebuilt_from (pair, side, number of pairs the version occurs in, diff markers in the chosen text), version_metadata (the version's metadata.jsonl record as released) and forum_metadata (the forum's record as released: title, authors, authorids, abstract, keywords, TL;DR, venue, venueid, _bibtex, pdf, dates, ...). Left out: article_pairs_small_test / mapping_small_test (a 468-article subset of test, same pairs). casimir_reviews: every note of reviews.jsonl (185,992 notes on 14,358 of the forums: official reviews, meta-reviews, decisions, author and public comments, withdrawal notices), one row each, split 'full' (the file has no split; meta.article_split gives the article's split). text = the note's content fields other than title, in release order; a note with one such field gets its value alone (e.g. the 'comment' of a comment), otherwise each field is a block ': ' (names as released, e.g. 'summary_of_the_paper', 'rating'; list values joined by ', '), blocks separated by a blank line; a note whose fields are all blank except the title (4 comments typed into the title field) gets its title as text, and one note that is blank everywhere has no text (dropped by the builder). title = content.title; date = tcdate; parent_id = forum id; author_id = the note's signature (an anonymous reviewer, area chair, authors or user group id); meta = the note's other fields as released (cdate, tcdate, tmdate, mdate, ddate, pdate, number, replyto, readers, writers, signatures, invitation(s), ...) plus content_fields (the field names in text order) and article_split. Everything in the release is written by people (article authors, reviewers, chairs); nothing is model-generated.

Licence: MIT (HF dataset card). The article texts remain their authors' work (public OpenReview PDFs), and the forum notes their writers' (reviewers, chairs, authors); cite CASIMIR and the OpenReview forum.

`casimir_reviews`

CASIMIR: A Corpus of Scientific Articles Enhanced with Multiple Author-Integrated Revisions. Jourdan, Boudin, Hernandez & Dufour (LREC-COLING 2024).

`chatbot_arena_140k`

Chatbot Arena human preference votes (arena-human-preference-140k and -55k). Chiang, Zheng, Sheng, Angelopoulos, Li, Li, Zhang, Zhu, Jordan, Gonzalez & Stoica (ICML 2024).

Two releases, one subset each, each with its one split train; every vote is kept (all languages). Each row is one user's vote between two anonymous models answering the same conversation. Model outputs are governed by the providers' terms, so messages holds the user turns with each assistant turn's content set to null, and the rows carry the licence of the user prompts. model is null because two models answered; their names and the vote are in meta. chatbot_arena_140k (lmarena-ai/arena-human-preference-140k, 135,634 votes, 2025-04 to 2025-07, CC BY 4.0): messages is the conversation of the vote (conversation_a; its user turns equal conversation_b's, checked on every row; all content items are text, joined if there are several). Votes after the first in a session (evaluation_order > 1) were cast on a conversation that continued earlier ones; when the release's full_conversation (the whole session) has other user turns than messages, they are kept in meta.full_conversation_user_turns (27,319 rows). meta keeps id, model_a, model_b, winner, evaluation_session_id, evaluation_order, conv_metadata, category_tag, language, is_code, timestamp as released, and conversation_b_num_tokens (the release's per-message token counts of conversation B). date is timestamp. chatbot_arena_55k (lmarena-ai/arena-human-preference-55k, 57,477 battles of 2023 to early 2024; the training set of the Kaggle 'LMSYS - Chatbot Arena Human Preference Predictions' competition, whose test set was never released; Apache-2.0): prompt in the CSV is a JSON list of the user turns, response_a / response_b the answers (left out); meta keeps id, model_a, model_b, winner_model_a, winner_model_b, winner_tie. The 55k release has no dates, so date is null.

Licence: arena-human-preference-140k: "User prompts are licensed under CC-BY-4.0, and model outputs are governed by the terms of use set by the respective model providers" (README; card tag cc-by-4.0). arena-human-preference-55k: card tag Apache-2.0, no further terms. Model outputs are not included. May contain unsafe or offensive text.

`chatbot_arena_55k`

Chatbot Arena human preference votes (arena-human-preference-140k and -55k). Chiang, Zheng, Sheng, Angelopoulos, Li, Li, Zhang, Zhu, Jordan, Gonzalez & Stoica (ICML 2024).

`chatgpt_detector_bias`

GPT Detectors Are Biased Against Non-Native English Writers (human-written data). Liang, Yuksekgonul, Mao, Wu & Zou (Patterns 2023).

The release has 10 subsets (925 texts), each a folder with data.json, name.json and per-detector score files. Included: every text of the 4 human-written subsets (name.json kind 'Human-Written'), 394 texts: Human_Data/TOEFL_real_91 (91 TOEFL essays by Chinese learners from the forum toefl.zhan.com, year <= 2020 per the paper), CollegeEssay_real_70 (70 US college admission essays from the prepscholar.com and collegeessayguy.com blogs), CS224N_real_145 (145 Stanford CS224N Winter 2021 project titles and abstracts, text 'Title: ...\nAbstract: ...' as released) and HewlettStudentEssay_real_88 (88 US 8th-grade essays from the Hewlett Foundation ASAP data). Left out, because they are AI-generated or AI-edited (name.json kind 'AI-Generated'), 531 texts: TOEFL_gpt4polished_91 (91, the TOEFL essays polished by GPT-4) and HewlettStudentEssay_GPTsimplify_88 (88, Hewlett essays simplified by GPT-4), both filed under Human_Data in the release, and GPT_Data/CS224N_gpt3_145 (145), CS224N_gpt3PromptEng_145 (145), CollegeEssay_gpt3_31 (31), CollegeEssay_gpt3PromptEng_31 (31). Code/ is not data. Nothing else is filtered (exact duplicates inside TOEFL_real_91 are kept as released). One row per data.json entry, in file order; source_record_id = ':<0-based index>'; text = 'document'; no titles, prompts, authors or URLs are released (prompt is empty). genre per subset. date is an upper bound, not the year of writing: TOEFL 2020 (paper: 'year<=2020'), CS224N 2021 (Winter 2021 quarter), Hewlett 2012 (the ASAP data were published in 2012); none for the college essays (collection dates undocumented). meta: subset, folder, name.json, 'type' (the college named for a CollegeEssay text), and detector_scores = for each detector file of the subset (Crossplag, GPTZero, HFOpenAI, OriginalityAI, Quil, Sapling, ZeroGPT; CS224N_real_145 has no Quil or Sapling file) the scalar fields of its entry for the text ('score', and GPTZero's perplexity, burstiness and probabilities; detector files list the texts in data.json order). In 6 CS224N_real_145 entries of GPTZero.json the scored text is the placeholder 'Title: ..\nAbstract: Project summaries unavailable', not the released abstract; those scores carry scored_document_differs = true. The detectors' raw API responses ('result_full': per-sentence HTML and the text again) are not kept.

Licence: MIT (README badge of the release and the authors' HF card; the GitHub repo has no LICENSE file; Zenodo lists 'other-open'). The texts were collected by the authors from third parties whose terms the release does not state: TOEFL essays from the forum toefl.zhan.com, college essays from the prepscholar.com and collegeessayguy.com blogs, abstracts from the Stanford CS224N project page, 8th-grade essays from the Hewlett Foundation ASAP competition data.

`civil_comments`

Civil Comments (Jigsaw Unintended Bias in Toxicity Classification). Borkan, Dixon, Sorensen, Thain & Vasserman, Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification (WWW 2019 companion).

Every comment of every split of the HF release: train 1,804,874, validation 97,320 (Kaggle test_public_expanded), test 97,320 (Kaggle test_private_expanded); 1,999,514 in all. One row per parquet row; text = text as released. meta = the seven Jigsaw labels as released (fraction of crowd raters who marked the comment: toxicity, severe_toxicity, obscene, threat, insult, identity_attack, sexual_explicit). The HF release has no comment ids, article ids, dates or author ids: source_record_id = '/' (row order of the parquet files at the pinned revision, from 0). date = '2017', an upper bound (the comments were written 2015-2017 and the platform shut down in 2017), not the year of writing. genre = 'news-site reader comment'. Rows with empty text are counted by the builder. Not used: the platform's own figshare archive (CC0, train portion only, with article ids and timestamps), which is a different release. Nothing is left out of the HF release.

Licence: CC0 1.0 (HF card 'license: cc0-1.0'; Jigsaw: 'This dataset is released under CC0, as is the underlying comment text').

`cmu_movie_summaries`

CMU Movie Summary Corpus. Bamman, O'Connor & Smith (ACL 2013).

Every plot summary of the release (plot_summaries.txt: 42,306 lines, 'Wikipedia movie idsummary'), one row each, in file order; no split ('full'). text = the summary as released (one paragraph; wiki-markup residue from the extraction kept). source_record_id = the Wikipedia movie id (page id; source_url = https://en.wikipedia.org/?curid=); title = Freebase movie name; date = 2012-11-02, the date of the Wikipedia dump the summaries were taken from (an upper bound: the summaries were written by Wikipedia editors at unknown earlier dates). meta = the movie's row of movie.metadata.tsv (Freebase id, name, release date, box office revenue, runtime, and the languages, countries and genres as {Freebase id: name} objects, parsed from the release's JSON strings; empty strings as released; meta.movie_metadata = false for the 99 summaries with no metadata row) and characters: the movie's rows of character.metadata.tsv in file order (character name, actor name, gender, date of birth, height, ethnicity Freebase id, age at release and the Freebase ids), each with the README's 'test data' persona labels of that character/actor map id where present (tvtropes from tvtropes.clusters.txt, name_cluster from name.clusters.txt). Left out: the metadata of the 39,534 movies without a summary (no text), and the optional CoreNLP-annotated copy of the summaries (a separate 628 MB download with the same texts).

Licence: CC BY-SA 3.0 US (README: 'All data is released under a Creative Commons Attribution-ShareAlike License'; the personas page links CC BY-SA 3.0 US). The summaries are English Wikipedia text (CC BY-SA). Attribution and share-alike.

`co_storm_wildseek`

WildSeek (Co-STORM: Into the Unknown Unknowns). Jiang, Shao, Ma, Semnani & Lam (EMNLP 2024).

All 100 rows of data.json (no split in the release, so the split is full). Each row is a topic and the user's goal (intent) as typed into the STORM web application (storm.genie.stanford.edu) in 2024; the authors sampled them from 6,608 unique topic-goal pairs (paper App. A). prompt = 'Topic: ' and 'Intent: ' on two lines (the release has the two fields only; the labels are the release's field names). category = domain (the authors' topic taxonomy path); meta keeps topic, intent and domain unchanged. The release has no ids; source_record_id = the row's index in data.json.

Licence: CC BY-SA 4.0 (dataset card, README and LICENSE).

`common_pile_foodista`

Common Pile v0.1 (raw): Ubuntu IRC, Foodista, Regulations.gov, USGPO, GitHub Archive. Kandpal, Lester, Raffel, Majstorovic, Biderman, Abbasi, Soldaini, Shippole, Cooper, Skowron, Longpre, Sutawika, Albalak, Xu, Penedo, et al. (arXiv 2025).

Every document of every document shard of five raw Common Pile v0.1 repos, one subset each: common_pile_ubuntu_irc (v0/documents, 8 shards), common_pile_foodista (v0/documents, 5), common_pile_regulations (v0, 7), common_pile_usgpo (data, 78), common_pile_github_archive (gharchive/v0/documents, 2,000). Split train (each repo's only split). These are the 'raw' repos, not the *_filtered versions used to train Comma v0.1 (those drop documents and redact PII). Not used: the repos' raw/ folders (copies before text extraction: IRC log files, Foodista HTML, GitHub raw threads), github_archive's licenses.jsonl (the repository licence table) and usgpo's licensed_pile_log.txt. One row per document, in shard order; source_record_id = '/' (e.g. 'ubuntu_irc/2015-04-17-#ubuntu-ir'; the repo name keeps ids unique across the five subsets); text = text as released (IRC: '[HH:MM] message' lines; Foodista: title, 'By:' and 'Published:' header lines, page text and comments; GitHub: the thread's posts converted from Markdown by Common Pile); title = title (regulations, usgpo; none for the others); date = created (ubuntu_irc: the log day; foodista: the page's published date; github_archive: the thread's creation time), posted_date (regulations) or date (usgpo); source_url = metadata.url; genre per subset ('IRC chat log (one channel, one day)', 'recipe and food web page', 'U.S. federal regulatory document', 'U.S. government publication', 'GitHub issue or pull request thread'). meta holds every other field as released: source, added, created / date / posted_date, document_type and agency (regulations), collection and author (usgpo), and the metadata dict (license, license_type and license_source for GitHub, url, authors, channel (IRC), repo (GitHub)), plus shard (the shard file). Documents with empty text (e.g. about a tenth of the regulations documents) are counted by the builder as dropped_empty. One id occurs twice in the release (regulations EPA-HQ-OAR-2002-0051-1991, two records of the same posted document that differ in added and url): the second occurrence in release order has source_record_id '...#2' and meta.id_occurrence 2 (ids are checked over all shards; raw/common_pile/duplicate_ids.json). Licence per document from metadata.license: 'Public Domain' -> public-domain (ubuntu_irc, regulations, usgpo), 'Creative Commons - Attribution - .../by/3.0/' -> cc-by-3.0 (foodista); GitHub's SPDX ids (either case) -> mit, apache-2.0, bsd-3-clause, cc0-1.0, cc-by-4.0, cc-by-sa-4.0, ... where the corpus has that licence id. GitHub documents under a licence the corpus has no id for (BSD-2-Clause, ISC, Unlicense, BSL-1.0, Zlib, WTFPL, MIT-0, 0BSD, Artistic-2.0, ...) are left out and counted per licence (raw/common_pile/_unmapped_licences/), because a row may only carry its own licence: 971,501 of the 30,318,774 GitHub threads (BSD-2-Clause 369,658, Unlicense 189,027, ISC 136,884, BSL-1.0 47,551, Zlib 42,019, WTFPL 41,548, MIT-0 25,960, 0BSD 19,532, Artistic-2.0 17,664, Vim 15,054, UPL-1.0 12,429, ECL-2.0 11,718, BSD-3-Clause-Clear 10,025, MS-PL 9,586, AFL-3.0 5,801, LPPL-1.3c 5,615, PostgreSQL 4,064, NCSA 3,200, BSD-4-Clause 2,629, MulanPSL-2.0 1,537; SPDX ids counted case-insensitively). Nothing else is filtered. Rows: ubuntu_irc 329,113 (+2 empty), foodista 72,034 (+56 empty), regulations 222,980 (+2,216 empty), usgpo 2,732,677, github_archive 29,347,255 (+18 empty), matching the documents counts in the repos' READMEs. Common Pile's own processing applies: it removed known bots' messages (IRC) and bot comments (GitHub), but automated posts remain (e.g. uptime-monitor issues); texts after 2022-11-30 (pre_chatgpt false) may contain machine-written text.

Licence: Per document, from Common Pile's metadata.license: Ubuntu IRC logs, Regulations.gov and USGPO documents are public domain (U.S. government works; IRC logs released into the public domain); Foodista content is CC BY 3.0; GitHub threads carry their repository's licence (GitHub terms: issue and PR text inherits the repository licence), as recorded by Common Pile. Common Pile warns that licence metadata can be wrong (licence laundering).

`common_pile_github_archive`

`common_pile_regulations`

`common_pile_ubuntu_irc`

`common_pile_usgpo`

`congressional_hearings`

Congressional hearings (govinfo CHRG). U.S. Congress, House, Senate and Joint Committee hearings (U.S. Government Publishing Office, govinfo collection CHRG).

One row per hearing package of the CHRG collection listed in govinfo's sitemaps (one sitemap per hearing year, 1936-2026, the year of the first held date for all but a handful; a package is one printed hearing, or one part of a multi-part hearing: 'CHRG-<h|s|j>hrg'). Nothing is sampled or filtered. text = the package's HTML text rendition (GPO's text of the printed hearing: cover and title pages, committee roster, contents, opening statements, testimony, questions and answers, prepared statements, material submitted for the record, as printed) with the HTML wrapper removed: the content of its

 block, verbatim, with GPO's hard line breaks, indentation, page and section rules and bracketed editorial notes. The only changes: links are replaced by their text; e-mail addresses that govinfo's Cloudflare front end replaces by an '[email protected]' link are decoded back to the address printed in GPO's file; HTML character references (& < >   · ...) are unescaped. GPO's typesetting codes in angle brackets are not HTML and are kept as printed: '', '', ''/'^{' (sub/superscript), '', '' (end of document), '}

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Papers for CoderBak/anagram-corpus

SlopBench: How Well Can We Rank Language Models by Slop? A Multi-Domain Benchmark of Repetitive AI Writing

Paper • 2609.33905 • Published 19 days ago

Can We Still Trace L1 Signals? Investigating the Resilience of Native Language Signals in the LLM Era

Paper • 2604.08568 • Published about 1 month ago

Detecting undisclosed LLM-generated content in parliamentary texts

Paper • 2606.14209 • Published Jun 12

'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions

Paper • 2606.04906 • Published Jun 3

NIRVANA: A Comprehensive Dataset for Reproducing How Students Use Generative AI for Essay Writing

Paper • 2604.07344 • Published Apr 8