Why do some models use so many em dashes?

The available measurements primarily show variation between models and prompts, while the explanation involving Markdown and post-training remains a hypothesis supported by one unreplicated preprint. Freeburg's protocol compares twelve models on matching essay tasks. A no-formatting prompt reduces Markdown markers but leaves dashes in several models' outputs. Provider differences are consistent with a post-training effect, without isolating that mechanism causally.

Does an em dash prove that AI wrote a text?

No, because dash frequency overlaps widely between human authors and models, and the available studies explicitly reject the character as a detector for individual texts. Freeburg's human sample ranges from 0.33 to 17.12 dashes per 1,000 words. Czuma studies a shift across 69,632 preprints without attributing each paper to AI. Editorial, academic or disciplinary decisions require independent and contextual evidence.

How can you reduce dashes in AI-assisted text?

Specify the required typographic convention, then review or count the characters, because compliance varies with the model, prompt and output length. Naming the exact character works better than a general instruction about Markdown. The final check should cover punctuation, syntax and rhythm rather than one glyph alone. A zero-dash rule is an editorial choice, not a detection method.

What is the French typographic convention for dashes?

French typography distinguishes em and en dashes, although their use in parenthetical clauses varies between style guides and publishers. The em dash can separate clauses or enclose a parenthetical insertion. The en dash serves ranges and, in some house styles, parenthetical clauses or list items. Spacing and final punctuation should follow the selected house style consistently.

Where does the balanced sentence in generated prose come from?

Research on diverse preferences, output diversity and sycophancy makes this form plausible, but none of the cited papers directly measures its frequency or cause. A single reward model represents diverse human preferences imperfectly. In Kirk et al.'s experiments, RLHF improves some generalisation while reducing output diversity. Sharma et al. show that agreement with a user's stated view can be rewarded.

Should writers ban em dashes?

The choice depends on the author's voice and house style, because a well-used dash remains legitimate punctuation and removing every instance does not improve every sentence. Keep it when it clarifies a genuine interruption or parenthetical thought. Replace it when two clauses would be clearer if ranked or separated. Apply the chosen convention consistently throughout the document.

Personal Effectiveness

Em dashes and AI writing: a style clue, never proof

28 August 2026 · Updated on 6 September 2026 · 9 min read · Paul-Antoine Tual

generative writing em dash typography stylometry RLHF alignment tokenisation BPE AI text detection French typography contrastive rhetoric

By Paul-Antoine TUAL, AI Transformation Leader, Growth and Transitions. August 2026, revised September 2026.

This article examines the em dash through three lenses that must remain separate if the argument is to stay sound: authorial taste, language-specific convention and patterns measured in human or generated corpora.

A punctuation habit may identify a writer’s voice without revealing the tool used.
A form accepted in French may look unusual under a particular English house style.
A statistical difference between groups cannot determine the origin of one text.

1. What 769 dashes say about this blog

The site’s archive from 19 July 2026 shows a pronounced editorial habit, but it cannot separate the contributions of the model, its prompt, human revision or a house style that emerged across publications.

The isolated corpus contains 61,496 words and 769 em dashes, or 12.50 per 1,000 words.
The median article has 11.55 per 1,000 words, with a range from 0.92 to 25.15.
Of 562 sentences containing at least one dash, 176 contain two or more.
Humans prompted, reviewed and edited these texts, so the count describes a mixed editorial process.

Comparison with the eight human essays in Freeburg’s preprint rules out turning this density into a test of authorship.

The mean in that human sample is 3.23 dashes per 1,000 words.
Its range runs from 0.33 to 17.12 depending on author and genre.
The blog’s historical density lies inside that range and creates no human-versus-machine boundary.

Corpus or model, without a suppression prompt	Em dashes per 1,000 words
This blog before July 2026	12.50
GPT-4.1	10.62
Claude Opus 4.6	9.09
DeepSeek V3	6.95
Human reference, mean of 8 essays	3.23
GPT-5.4	1.43
Llama 3.1 and 3.3 Instruct	0.00

These figures from one protocol help compare its experimental conditions, but they do not rank all human writers or every present and future version of each model family.

The models, parameters and calls belong to experiments run in February and March 2026.
The human sample contains only eight essays and is not a general benchmark.
Changing the prompt substantially alters several rows in the table.

2. The Markdown hypothesis and its limits

The preprint The Last Fingerprint proposes that the em dash survives as prose punctuation when other structural habits learnt from marked-up text are suppressed, but its experiment tests a response asymmetry rather than the causal composition of training data.

Twelve models from five providers generate roughly 240,000 words on ten topics and under two principal conditions.
An instruction to write without headings, lists or bold removes almost all those visible markers.
Dashes persist in several models: GPT-4.1 falls from 10.62 to 9.10 per 1,000 words, while Claude Opus 4.6 falls from 9.09 to 0.19.
The author presents the Markdown genealogy as a falsifiable proposal because controlled training corpora with and without markup are unavailable.

Differences between models make a role for post-training and system instructions plausible, without identifying one procedure as the measured cause.

The two tested Llama models produce no dashes in the principal conditions.
The Llama 3.1 8B base model produces a few, while its instruction-tuned counterpart reaches zero.
Providers may differ in data, tokenisers, settings and alignment methods.
The outputs are observed evidence; the proposed mechanism remains an inference.

3. What tokenisation establishes, and what it does not

Inspection of OpenAI’s public vocabularies establishes that some sequences containing the em dash have dedicated tokens, but a token’s presence or rank cannot by itself explain the probability that a model will choose the character.

In cl100k_base, 44 tokens contain the em dash and 7 contain the en dash.
The sequence space + em dash has rank 2001, while the standalone character has rank 2345.
English tokens such as —and, —but and —the exist, while the French sequences —et, —le and —ce do not.
OpenAI’s current public mappings associate GPT-4.1 with o200k_base, not cl100k_base.

The economic interpretation must therefore remain modest: a merged segment may require fewer tokens than a split sequence, while generation still depends on learnt probabilities, context and post-training.

Token count describes an encoding rather than a syntactic intention.
An English encoding advantage does not demonstrate an erroneous transfer into French.
Any relevant comparison must use the tokeniser actually associated with the tested model.

4. The balanced sentence remains a rhetorical hypothesis

Three alignment papers help explain why cautious, accommodating or less diverse prose may emerge, but none experimentally connects those mechanisms to the frequency of sentences shaped as ‘not X, but Y’.

Chakraborty et al. show the limits of one reward model when human preferences differ.
Kirk et al. observe, across two base models and two tasks, that RLHF generalises better out of distribution than supervised fine-tuning while producing less diverse outputs.
Sharma et al. find that responses matching a user’s stated view are more likely to be preferred in human comparison data.
Connecting these results to one sentence form is an editorial interpretation rather than a result measured in those studies.

The blog’s historical corpus provides only a starting point for a proper analysis of this construction.

Twenty-six constructions resembling ‘not X, but Y’ were counted in 61,496 words.
The resulting density of 0.42 per 1,000 words is descriptive and has no comparable control corpus.
Revision should judge each contrast by its argumentative function rather than erase it as a supposed AI signature.

5. French and English conventions are not interchangeable

Gérald Purnelle’s French typography syllabus distinguishes the long dash used between clauses from the en dash used in some lists and ranges, while recognising that typographic practice contains variants and exceptions.

French can use the em dash to enclose a parenthetical clause or mark a syntactic break.
The en dash commonly marks ranges and may replace the em dash under a publisher’s house style.
French spacing around parenthetical dashes often differs from the closed-up em dash common in American publishing.
Final punctuation generally replaces the closing dash when the parenthetical clause ends the sentence.

English practice also depends on house style, so a British editor may prefer a spaced en dash where an American publisher chooses a closed em dash without either convention identifying the writer as human or artificial.

Character choice belongs to the publication’s convention.
Spacing belongs to that same convention and should not be mixed casually.
Consistency and readability matter more than treating one national practice as universal English.

The check on the blog’s former French corpus finds coherent French spacing, shifting the editorial question from correctness to the rhythm the author wants.

Of 769 em dashes, 760 have spaces on both sides, or 98.8%.
None sits immediately before a full stop.
Neither observation proves authorship or sentence quality.
High frequency may burden prose even when every occurrence follows the chosen style.

6. A population signal cannot judge one text

Przemysław Czuma’s preregistered study measures a rise in the share of Discussion sections containing at least one em dash across 69,632 medRxiv preprints, then states that its design establishes neither causation nor the origin of an individual paper.

Prevalence rises from 4.23% before 30 November 2022 to 11.58% afterwards.
The estimated absolute increase is 7.35 percentage points, with a 95% confidence interval from 6.94 to 7.77.
The annual series reaches 8.0% in 2024 and 20.3% in 2025.
Language-model use is one possible explanation among wider changes in writing and editing practice.

The practical consequence is firm: dash use may contribute to aggregate stylometric research, but it must never support an accusation, penalty or rejection on its own.

Human authors have widely differing punctuation habits.
Models change with version, prompt and generation context.
Human revision further obscures any attribution based on one character.
An individual assessment should examine sources, working process and independent evidence.

7. Set a verifiable editorial rule

An organisation publishing AI-assisted text benefits from defining a restrained convention, applying it to both people and tools, and reviewing the reasoning organised by the punctuation.

Put an example of the expected punctuation in the prompt or style guide.
Count characters automatically when a formal limit must be met.
Revisit sentences with several interruptions and decide which clause is primary.
Keep a dash when it genuinely serves the author’s voice and the reader’s understanding.

This site’s zero-em-dash rule for ordinary prose expresses a preference for more direct sentences and makes no claim to correct all French typography or reveal how a text was produced.

It allows a simple automated check before publication.
It often turns an aside into a sentence, comma or parenthesis.
It can make room for quotations, linguistic examples and documents governed by another style guide.

If you want to connect editorial choices with your organisation’s actual AI practices, the Junyr maturity audit is a free, no-commitment 30-minute video call.

You locate the organisation on the maturity scale.
You identify the main blocker and the first sensible project.
You receive a one-page follow-up after the call.

Measurement limits

The quoted densities describe dated protocols and corpora, providing neither a universal threshold nor a detector for individual texts.

Model values come from one preprint with no known independent replication as of 6 September 2026.
The blog and vocabulary figures are reproducible internal measurements dated 28 August 2026.
Future comparisons should specify the version, tokeniser, prompt and human reference corpus.

Sources

[1] E. M. Freeburg, The Last Fingerprint: How Markdown Training Shapes LLM Prose, arXiv:2603.27006, 27 March 2026.

[2] P. Czuma, Em-ergence of the em-dash, arXiv:2606.29540, 28 June 2026.

[3] S. Chakraborty et al., MaxMin-RLHF: Alignment with Diverse Human Preferences, ICML 2024.

[4] R. Kirk et al., Understanding the Effects of RLHF on LLM Generalisation and Diversity, ICLR 2024.

[5] M. Sharma et al., Towards Understanding Sycophancy in Language Models, ICLR 2024.

[6] G. Purnelle, Règles et usages de typographie française, University of Liège.

[7] OpenAI, tiktoken_ext/openai_public.py and tiktoken/model.py, public encoding files and model-to-tokeniser mappings.

[8] Internal measurement on this site’s archive at 19 July 2026: 24 French articles and 61,496 prose words excluding frontmatter and sources.

Frequently asked questions

Why do some models use so many em dashes?: The available measurements primarily show variation between models and prompts, while the explanation involving Markdown and post-training remains a hypothesis supported by one unreplicated preprint.

Freeburg's protocol compares twelve models on matching essay tasks.
A no-formatting prompt reduces Markdown markers but leaves dashes in several models' outputs.
Provider differences are consistent with a post-training effect, without isolating that mechanism causally.
Does an em dash prove that AI wrote a text?: No, because dash frequency overlaps widely between human authors and models, and the available studies explicitly reject the character as a detector for individual texts.

Freeburg's human sample ranges from 0.33 to 17.12 dashes per 1,000 words.
Czuma studies a shift across 69,632 preprints without attributing each paper to AI.
Editorial, academic or disciplinary decisions require independent and contextual evidence.
How can you reduce dashes in AI-assisted text?: Specify the required typographic convention, then review or count the characters, because compliance varies with the model, prompt and output length.

Naming the exact character works better than a general instruction about Markdown.
The final check should cover punctuation, syntax and rhythm rather than one glyph alone.
A zero-dash rule is an editorial choice, not a detection method.
What is the French typographic convention for dashes?: French typography distinguishes em and en dashes, although their use in parenthetical clauses varies between style guides and publishers.

The em dash can separate clauses or enclose a parenthetical insertion.
The en dash serves ranges and, in some house styles, parenthetical clauses or list items.
Spacing and final punctuation should follow the selected house style consistently.
Where does the balanced sentence in generated prose come from?: Research on diverse preferences, output diversity and sycophancy makes this form plausible, but none of the cited papers directly measures its frequency or cause.

A single reward model represents diverse human preferences imperfectly.
In Kirk et al.'s experiments, RLHF improves some generalisation while reducing output diversity.
Sharma et al. show that agreement with a user's stated view can be rewarded.
Should writers ban em dashes?: The choice depends on the author's voice and house style, because a well-used dash remains legitimate punctuation and removing every instance does not improve every sentence.

Keep it when it clarifies a genuine interruption or parenthetical thought.
Replace it when two clauses would be clearer if ranked or separated.
Apply the chosen convention consistently throughout the document.

Paul-Antoine Tual

AI Transformation Leader · Junyr Method™ · Transition manager specialising in AI for French SMEs and mid-caps. Engineer from the École des Mines de Nantes, lawyer, developer since 1993.

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