MARGO-full contains the full text of 4,000 opinion articles that are copyrighted by their original publishers. It is shared only for non-commercial research. Requests are reviewed manually. If you do not need the article text, use the public MARGO dataset instead, which has every annotation and a link to each article.

MARGO-full — Multilingual Argumentative Op-eds (with full text)

This is the gated, full-text version of MARGO. It is identical to the public MARGO dataset, plus the text of the human-written articles: the article bodies (text_clean, old), the segmented articles the extractions refer to (rendered), the verbatim quotes in the extractions, and the raw extraction outputs. If you only need the annotations, metadata and generated articles, use the public version.

MARGO is a corpus of 4,000 human-written opinion articles in eight languages, paired with 43,750 LLM-generated opinion articles written from the same sources by five language models under six prompt conditions. Every LLM-generated article was written from the topic, event and question of one specific human article, so human and LLM texts can be compared source by source.

The corpus also includes two argument extractions for every article: a discourse-structure extraction (the main claim, sub-claims and counter-claims, and how the text unfolds move by move) and a Toulmin extraction (the grounds, warrant, backing, qualifier and reservations of every claim).

Languages: English, French, Spanish, Hindi, Russian, Japanese, Polish, Bulgarian (500 human articles each). Models: GPT-5.6 Sol, Gemini 3.8 Flash, Opus 5, DeepSeek 4.1 Flash, Qwen-Max.

Terms of use. The human-written articles are copyrighted by their original publishers and are shared here for non-commercial research use only. Do not redistribute them or any substantial part of them, including in derived datasets. You may publish short quotations for scholarly discussion. To share annotations derived from this data, point to the public MARGO dataset.

Configs

The dataset has five configs. Each has a single train split (MARGO is a corpus, not a train/test benchmark). All configs join on text_id.

Config	Rows	What a row is
`human`	4,000	One human-written opinion article
`generated`	43,750	One LLM-generated article (conditions P1–P4, P5b, P6)
`critiques`	2,000	One LLM self-critique (condition P5a; notes, not articles)
`discourse_structure`	47,750	The discourse-structure extraction of one article (human or generated)
`toulmin`	47,750	The Toulmin extraction of one article (human or generated)

Loading

from datasets import load_dataset

REPO = "gurneetgill/MARGO-full"
human     = load_dataset(REPO, "human", split="train")
generated = load_dataset(REPO, "generated", split="train")
critiques = load_dataset(REPO, "critiques", split="train")
structure = load_dataset(REPO, "discourse_structure", split="train")
toulmin   = load_dataset(REPO, "toulmin", split="train")

Joining

import pandas as pd

h = human.to_pandas()
g = generated.to_pandas()

# Every vanilla (P1) article next to its human source
p1 = g[g.condition == "P1"].merge(h, left_on="source_id", right_on="text_id",
                                  suffixes=("_llm", "_human"))

# Human title: use title_clean if present, else title
h["title_final"] = h.title_clean.fillna(h.title)

Every generated source_id matches a human text_id, every extraction text_id matches an article in human or generated, and every body_sha256 matches its text. All three were checked for this release.

`human`

Signed argumentative opinion pieces (op-eds, columns, tribunes) by named authors, published 2016–2022 and originally written in the language of the record. Articles were collected from outlet archives and passed automated genre, origin and argumentativeness checks. Boilerplate such as author bios, newsletter widgets and photo credits was removed. About 20% of articles were also reviewed by hand.

Column	Description
`text_id`	Unique ID; the join key (`source_id` in `generated`). Usually `<lang>_<hash>` (e.g. `bul_6b0d2af42da0`), with a few legacy IDs (e.g. `018-lemonde-fr`).
`language`	`english`, `french`, `spanish`, `hindi`, `russian`, `japanese`, `polish`, `bulgarian`
`title`	Title as collected
`title_clean`	Cleaned title, present for 1,059 records. Use `title_clean` if present, else `title`.
`text_clean`	The article body to use. Boilerplate removed.
`old`	The body as originally collected, present only for the 2,965 records whose body was cleaned. If `old` is null, `text_clean` is the original text.
`author`	Author name(s)
`source`, `source_domain`, `url`	Outlet name, outlet web domain, article URL
`published_date`	Publication date at the precision available (some are month–year only, e.g. `05-2019`; do not fill in missing days)
`publication_year`	Year
`debate_topic`	The question the article addresses, as an English sentence (e.g. "Should food donations be exempt from VAT?")
`topic_key`	Short topic key, merged across paraphrases and languages
`stance`	Coarse stance on `debate_topic`: `pro` (2,332), `contra` (1,557), `mixed` (111)
`stance_confidence`	Confidence of the stance label (1–5)
`tokens_o200k`	Length of the article in o200k_base tokens in its own language
`en_equiv_tokens`	Estimated length in English-equivalent tokens (used for sampling; mean 1,315)
`metadata_extra`	JSON string with provenance and quality fields (see below). Fields vary by record; parse with `json.loads`.

Provenance and quality fields (inside `metadata_extra`)

These come from collection and review and are mainly useful for sensitivity checks. In most analyses you can ignore them. Internal review notes and evidence records from curation are not included in this release.

Field	Description
`collection`, `collection_detail`	Collection round
`genre`, `source_category`, `genre_judge`	Genre labels and automated genre review
`quality_gates`, `audit`, `strict_gate`, `strict_gate_conf`, `arg_share`	Automated genre/argumentativeness checks (all 4,000 passed the strict gate)
`residue_removed`, `residue_flags`	What the cleaner removed, and known issues it could not fix
`full_text_review_revision`, `final_audit_revision`, `final_release_revision`, `author_metadata_revision`, `text_cleanup_revision`	Tags marking records that received later manual corrections, where they apply
`word_count`, `count_unit`, `word_count_clean`	Legacy length counts (words; characters for Japanese). Prefer `tokens_o200k`.
`core_matched_250`, `in_length_window`, `era`	Sampling flags. `core_matched_250` is not the 400-source subset used for prompt conditions P2–P6.

import json
h["residue_flags"] = h.metadata_extra.map(lambda s: json.loads(s).get("residue_flags"))

`generated` and `critiques`

Column	Description
`text_id`	`<model>__<source_id>__<condition>__<run>[__piece<k>]`, e.g. `opus-5__018-lemonde-fr__P1__1`
`model`	Generating model: `gpt-5.6-sol`, `gemini-3.8`, `opus-5`, `deepseek-4.1-flash`, `qwen-max` (see mapping below)
`source_id`	`text_id` of the human source article
`condition`	Prompt condition: `P1`, `P2`, `P3`, `P4`, `P5b`, `P6` in `generated`; `P5a` in `critiques`
`run`	Generation run (integer). `1` except in P3, where runs are `2`–`5`
`piece`	P4 only: which of the five articles from one call (`1`–`5`); null otherwise. Not present in `critiques`.
`language`	Language of the source article (and of the generated text)
`title`, `text`	Title and body of the generated article (`generated` only)
`body_sha256`	SHA-256 of `text` (UTF-8) for integrity checks (`generated` only)
`critique`	The model's critique of its own P1 article (`critiques` only)

Prompt conditions

Condition	Name in paper	Sources (per model)	Records (per model)	What it is
`P1`	Vanilla	4,000	4,000	Baseline. The model gets the topic, event and question of the human source article and writes an opinion article in the source's language, for the same type and country of publication, as of the source's publication date, at about the source's length. Instructions are in English.
`P2`	Guided	400	400	Vanilla plus the source author's stance and a short author profile (stance, main claim and role)
`P3`	Repeated sampling	400	1,600	Four more independent vanilla generations, runs `2`–`5` (run `1` is the P1 article)
`P4`	Diversified	400	2,000	Five different articles requested in one call, `piece` 1–5, differing in stance, reasons, claims and order
`P5a`	Edited (critique step)	400	400	The model critiques its own P1 article. These are notes, not articles (`critiques` config).
`P5b`	Edited (revision)	400	400	The model revises its P1 article using the P5a critique
`P6`	Target language	350	350	Vanilla, but with the instructions written in the article's language; non-English sources only

Subsets. P2–P5 use the same 400 sources (50 per language) for every model. P6 uses those 400 minus the 50 English sources (350). P1 covers all 4,000 sources.

Models.

`model`	Name in paper
`gpt-5.6-sol`	GPT-5.6 Sol
`gemini-3.8`	Gemini 3.8 Flash
`opus-5`	Opus 5
`deepseek-4.1-flash`	DeepSeek 4.1 Flash
`qwen-max`	Qwen-Max

Argument extractions (`discourse_structure`, `toulmin`)

Both extractions work on the article split into sentences, and refer to the text by sentence ID rather than by quoting it: the title is T, and sentence k of paragraph block n is n.k. The Toulmin extraction builds on the claim list of the discourse-structure extraction, so the two use the same claim IDs (C for the main claim; S1, S2, … for sub-claims). The P5a critiques are not articles and were not extracted.

Both configs start with the same identifying columns:

Column	Description
`text_id`	The article this extraction describes (joins to `human.text_id` or `generated.text_id`)
`origin`	`human` or `generated`
`source_id`	`text_id` of the human source article (equal to `text_id` for human articles)
`generator_model`	Generating model for generated articles (as in `generated.model`); null for human articles
`condition`	Prompt condition for generated articles; null for human articles
`language`	Language of the article

`discourse_structure`

Column	Description
`title`	Title of the article
`rendered`	The segmented article exactly as the extractor saw it: `TITLE [T]: …`, paragraph markers `¶n` (with `(heading?)` hints on likely headings), and each sentence prefixed with its ID `[n.k]`
`sids`, `n_sids`, `n_blocks`	The list of sentence IDs, the number of sentences and the number of paragraph blocks
`A`	Step 1, the argument outline (struct): `issue`, `central_claim`, `sub_claims`, `counter_claims`, `rebuttals`, `extension_claims`, `layout`, `genre_flag`, `notes` (see below)
`B`	Step 2, the deployment map (struct): `moves`, `claim_statements`, `signposts`, `digressions`, `callbacks`, `outline_gaps`, `flow_summary`, `notes` (see below)
`issues_A`, `issues_B`	Non-fatal warnings from the automatic checks, and any normalisation applied (e.g. a rebuttal that duplicated `C` was removed, which can leave a gap in the IDs: `R1`, `R2`, `R4`). Empty if none.
`model_a`, `model_b`, `model`	The models that ran each step (`gpt-5.6-sol`; `gemini-3.7-flash`, or the Opus 5.5 fallback); `model` is `hybrid`
`status`, `stage`, `version`, `prompts`, `run`, `created_at`	Run status (`SUCCESS`), prompt version (`structure_extraction_prompt_v2_6.txt`), extraction attempt and time
`meta_A`, `meta_B`	JSON strings. Run metadata: `model`, token counts and the raw model output (`raw_result`, before normalisation)
`text_field`	Which field of the article was extracted
`instrument_hash`, `input_fingerprint`, `configuration_fingerprint`, `a_fingerprint`, `batch_id`, `batch_created_at`, `request_id`	Hashes and IDs that tie the output to its exact input, prompt and settings

Article metadata is not repeated here; join to human or generated on text_id.

Outline (A)

Field	Values
`genre_flag`	`argumentative` or `non_argumentative`
`layout[]`	`{block, kind}`: non-prose blocks; `kind` = `HEADING`, `STANDFIRST`, `CAPTION`, `EPIGRAPH`, `BOILERPLATE`, `OTHER_NONPROSE`. Unlisted blocks are body prose.
`issue`	`{english, posed_sid, explicit}`: the question the article answers, where it is posed (null if only implied)
`central_claim`	`{id: "C", original, english, type, explicit, first_sid, best_sid, title_states_claim, hedged, hedge_quote}`; `type` = `FACT`, `CAUSE`, `VALUE`, `POLICY`, `DEFINITION`
`sub_claims[]`	`{id: "S1"…, original, english, sid, supports}`; `supports` = `C` or another `S` id
`counter_claims[]`	`{id: "CC1"…, original, english, sid, attacks, stance, voiced_by}`; `stance` = `ATTRIBUTED`, `ANTICIPATED`, `CONCEDED`
`rebuttals[]`	`{id: "R1"…, original, english, sid, rebuts}`; `rebuts` = a `CC` id
`extension_claims[]`	`{id: "X1"…, original, english, sid}` (rare)
`language`, `notes`	Language as written by the model; free-text notes on uncertain choices

original is in the article's language (the author's wording where possible), english is an English paraphrase, and sid is the sentence where the element is first stated.

Deployment map (B)

Field	Values
`moves[]`	`{id: "M1"…, from, to, role, node_ids, support_kinds, point_sid, label, gist, mode}`. Moves cover every sentence once, in order. `role` = `OCCASION`, `HOOK`, `BACKGROUND`, `ISSUE`, `ANNOUNCE`, `THESIS`, `ARGUMENT`, `COUNTER`, `REBUTTAL`, `DIGRESSION`, `IMPLICATION`, `CALL`, `CLOSE`, `EXTENSION`, `OTHER`. `node_ids` = outline ids served. `support_kinds` (for `ARGUMENT`, `REBUTTAL`, `IMPLICATION`) = `ANECDOTE`, `EXAMPLE`, `STATISTIC`, `TESTIMONY`, `AUTHORITY`, `COMMON_GROUND`, `ANALOGY`, `PRINCIPLE`, `HYPOTHETICAL`, `REASONING`. `point_sid` = the sentence stating the move's point (`ARGUMENT`, `REBUTTAL`, `IMPLICATION` only). `mode` = `NARRATE`, `DESCRIBE`, `DEFINE`, `EXPLAIN`, `EVALUATE`, `PRESCRIBE`, `QUESTION`.
`claim_statements[]`	`{sid, kind}`: every sentence stating `C`; `kind` = `FULL`, `PARTIAL`, `TITLE` (sid `T`), `STANDFIRST`
`signposts[]`	`{sid, kind, quote}`; `kind` = `ANNOUNCE`, `SEQUENCE`, `STAGE`, `SHIFT`, `ENDOPHORIC`, `HEADING`, `THESIS_MARK`, `QUESTION_FRAME`
`digressions[]`	`{from, to, departs_from, relatedness, framing, marker_quote, resumed_at}`; `relatedness` = `ELABORATIVE`, `TANGENTIAL`; `framing` = `MARKED`, `UNMARKED`. Spans match the `DIGRESSION` moves.
`callbacks[]`	`{at, recalls, quote}`: returns to earlier non-claim material, e.g. the opening anecdote
`outline_gaps[]`	`{sid, english}`: claims the map found that the outline had no node for
`flow_summary`, `notes`	≤60-word description of the argument's path, citing move ids; free-text notes

Because Parquet stores each nested field with one shared structure, a field that is absent in a particular article appears as null.

`toulmin`

Column	Description
`claims_in`	The claims taken from the article's discourse-structure outline: the central claim and sub-claims only (`id` = `C`, `S1`, …), with `sid`, `original`, `english` and, for sub-claims, `supports`. Counter-claims and rebuttals are not annotated. The Toulmin extractor did not detect claims itself.
`claims`	One Toulmin annotation per claim in `claims_in` (fields below)
`rendered`	The segmented article, identical in form to the discourse-structure `rendered`
`issues`	Non-fatal warnings from the automatic checks (empty if none)
`model`, `prompts`, `prompt_tag`, `run`, `status`, `created_at`	`gemini-3.7-flash`, `toulmin_prompt_v3.txt`, `v3`, extraction attempt, `SUCCESS`, time. For 65 articles these run fields (and the provenance fields below) were not recorded and are null; their annotations are complete.
`meta`	JSON string. Run metadata: `model`, token counts and the raw model output (`raw_result`)
`source`, `struct_fingerprint`, `configuration_fingerprint`, `batch_id`, `request_id`	Provenance: `source` = `outline` (claims came from the structure outline); `struct_fingerprint` ties the row to the structure extraction it used

Each entry of claims:

Field	Description
`id`	The claim (`C`, `S1`, …), matching `claims_in`
`grounds`	List of `{sids, kind, claim_ref}`. `kind` is `STATISTIC`, `EXAMPLE`, `ANECDOTE`, `TESTIMONY`, `AUTHORITY`, `OBSERVATION`, `PRINCIPLE`, `DEFINITION`, `REASONING`, or `CLAIM` (support from another claim, named in `claim_ref`)
`warrant`	`{status, english, sid, type}`: `status` is `EXPLICIT` (with its `sid`), `IMPLICIT` (reconstructed by the extractor) or `ABSENT` (no grounds to bridge); `english` states the warrant as a general rule in one sentence (empty if `ABSENT`); `sid` is null unless `EXPLICIT`; `type` is `CAUSE`, `SIGN`, `GENERALIZATION`, `PARALLEL`, `CLASSIFICATION`, `AUTHORITY`, `MOTIVATIONAL` (shared values or goals), or `NONE` when the warrant is `ABSENT`
`backing`	Sentence IDs that support the warrant itself rather than the claim (a law, theory, body of research, credentials; often empty)
`qualifier`	`{present, quotes, sids, strength}`: markers that change how certainly or how widely this claim is asserted (e.g. "probably", "often"), quoted verbatim with one sentence ID per quote; `strength` = `HEDGED` (weakens or limits), `STRONG` (increases commitment) or `NEUTRAL` (no qualifier: `present` = false and empty lists)
`rebuttals`	Reservations the author places on the claim (Toulmin's "unless"), as `{sids, kind}` with `kind` = `EXCEPTION` ("unless", "except when"), `CONDITION` (holds only if …) or `CONCESSION` (a contrary point granted about this claim). Despite the field name, these are not the rebuttal nodes (`R1`, …) of the discourse-structure outline, which answer an opposing view.
`notes`	Short English note, only when something was ambiguous (often empty)

Using an extraction

import re
from datasets import load_dataset

structure = load_dataset(REPO, "discourse_structure", split="train")
toulmin = load_dataset(REPO, "toulmin", split="train")

def sentences(ex):
    """Map sentence ID -> sentence text, from the segmented article."""
    return dict(re.findall(r"^\[(\d+\.\d+)\] (.*)$", ex["rendered"], flags=re.M))

s = structure.filter(lambda r: r["text_id"] == "bul_0a68fc291d8c")[0]
sent = sentences(s)
c = s["A"]["central_claim"]
print(c["english"], "|", sent[c["best_sid"]])
for m in s["B"]["moves"]:
    print(m["id"], m["role"], m["from"], "-", m["to"], m["gist"])

t = toulmin.filter(lambda r: r["text_id"] == "bul_0a68fc291d8c")[0]
for cl in t["claims"]:
    print(cl["id"], [g["kind"] for g in cl["grounds"]], cl["warrant"]["type"], cl["qualifier"]["strength"])

How the extractions were made

Discourse structure (prompt structure_extraction_prompt_v2_6), two steps per article:

Argument outline (A; GPT-5.6 Sol). The issue the article addresses; the central claim C (wording, type: fact, cause, value, policy or definition; whether it is explicit or hedged; where it is first and most fully stated; whether the title states it); sub-claims S1… with the claim each one supports; counter-claims CC1… with their stance (attributed, anticipated or conceded); rebuttals R1…; and rare extension claims X1…. Non-prose blocks (headings, standfirsts, captions) are also listed.
Deployment map (B; Gemini 3.7 Flash, temperature 0). The whole text is divided into consecutive moves, each a span of sentences doing one job. Each move records the outline claims it serves, the kinds of support it uses, its dominant discourse mode, and a one-sentence gist. The map also lists every sentence that states the central claim, signposts (e.g. "first", "in conclusion"), digressions, callbacks to earlier passages, and a short flow summary.

In the prompt, the move role THESIS and the signpost kind THESIS_MARK refer to statements of the central claim C.

Every output passed automatic checks (valid JSON, every sentence ID exists, consistent references, and moves covering the text with no gaps or overlaps); failed outputs were rerun under the same settings. Articles whose deployment map failed three times were run once more with Claude Opus 5.5 using the same prompt. No output was edited by hand. Native speakers checked the extractions of 20 texts and rated 94.7% fully correct and 4.7% partially correct.

Toulmin (Gemini 3.7 Flash, temperature 0; prompt toulmin_prompt_v3). For every claim in the outline (C, S1, S2, …) the extractor records the grounds, warrant, backing, qualifier and reservations described above. Every output passed automatic checks (valid JSON, every sentence ID exists, exactly one annotation per outline claim, valid claim_refs and warrant sentence IDs); failed outputs were rerun with the same prompt and model, with no fallback model. No output was edited by hand. Native speakers checked the extractions of 20 texts and found 91% completely accurate and 8% partially accurate.

Things to know before analysing

Outlets are nested within languages and are unevenly represented (44 outlets in total, and some languages are dominated by one or two outlets). Language differences in the human articles cannot be fully separated from outlet, author or genre effects. Keep source_domain for outlet-level controls.
The date window includes late 2022, so not every article predates ChatGPT. Restricting to 2016–2022 was meant to reduce the chance of AI-assisted writing, not rule it out.
Cleaning was largely automated. About 20% of articles were checked by hand. Some residual extraction errors may remain; see residue_flags in metadata_extra.
debate_topic, topic_key and stance were assigned by an LLM. They are not human gold labels.
The extractions are model outputs, not gold annotations. They were spot-checked by native speakers on 20 texts each. Ground kinds and warrant types are the least reliable Toulmin labels, so read them as a profile across many claims rather than case by case.
Use the stored segmentation for sentence IDs. Extractions cite sentences as n.k. Map them to text with the row's own rendered field (see the example above), not by re-splitting the article, because a different sentence splitter would give different IDs.
The LLM-generated articles are model outputs. They can contain factual errors or invented details, quotes and figures. Do not treat them as factual or attribute them to real people.

Not included in this release

The feature-discovery labels used in the paper (50 features per article) and the feature codebook are not included, nor are the full generation prompts. These are available on request.

License

MARGO-full is released under custom terms; see the LICENSE file.

Annotations, metadata and generated data are licensed under CC BY-NC 4.0, the same as the public MARGO dataset.
The human-written articles (text_clean, old, rendered, and verbatim quotations of article text) remain the copyright of their publishers and authors. No license is granted to them. They are provided only for non-commercial research by approved users, who agree not to redistribute them.
Generated articles and critiques: users are responsible for complying with any terms the model providers place on the use of model outputs.

Rights holders who want an article removed can contact the authors through the Community tab.

Citation

If you use MARGO, please cite:

@misc{gill2026llms,
  title  = {{LLM}s Redraw Argumentative Patterns Across Languages},
  author = {Gill, Gurneet and
            Kaur, Jasleen and
            Maltcheva, Stella and
            Duong, Khoa Ha Minh and
            {Anasco Santillan}, Rodrigo Armando and
            Kapitanenko, Maria and
            Gupta, Ankita and
            Karpinska, Marzena},
  year   = {2026},
  note   = {Preprint}
}

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