Si machina nullo moderatore aberrasset...
About
This dataset features human authored text, mined from Acervus, annotated by multiple LLMs with 5 different span relations and 4 different categorical features. The organic examples are paired with machine authored modifications.
It also contains a number of hallucinated or adversarial Latin-like examples which pass lexical filters but are semantically or grammatically incoherent. Additionally, there are some 10,000 samples created by a variety of Lorem Ipsum generators.
These include highly adversarial samples which contain imagined Latin forms, no discernible syntax, or are completely incoherent.
Atrius Tubulus consul centum senatus consultum triumphi non sregio pugnāvis
ob sidera accipiet aga specta dimidia parte multa vis sed
Pseudo Latin can be observed across multiple different registers, further complicating detection:
conglutinatus est in hodie restandum quod vobis Et terra venter noster Clamabat que
ad dixerunt Monstra calumniatus nos Dominus
Relations
- NLI Logical Entailment (premise-hypothesis)
- Question Answering (question-answer)
- Quote Attribution (quote-attribution)
- Dialogue e.g. chat (utterance-reply)
- Prompt-Response, e.g. temporal succession, narrative sequence, or thematic association (prompt-response)
Categorical
- Text Genre (ecclesiastical, historiography, law, treatise, poetry, rhetoric, general, satire)
- Named Entities
- UPOS and UFEAT Annotation
- Adversarial Highly-Latin-Like Text (Pseudo-Latin, Lorem Ipsum, & Hallucinated Latin that evaded aggressive lexical filtering)
Machine Modification
- Human Original vs. Machine Authored Back Translation (human-machine)
- Original vs. Machine Authored Translation (lat-eng)
Total rows: 137026
models:
| Model | Count |
|---|---|
| claude-opus-5-5 | 66762 |
| gpt-6.1-sol | 58268 |
| LoremGenerators | 11996 |
categories:
| Category | Count |
|---|---|
| treatise | 32163 |
| ecclesiastical | 28648 |
| pseudo | 21385 |
| historiography | 19288 |
| poetry | 14985 |
| law | 6893 |
| general | 6740 |
| rhetoric | 6312 |
| satire | 612 |
| pos-morph-tagging | 3378 |
| entity-recognition | 9713 |
relations:
| Relation | Count |
|---|---|
| machine-authorship | 40003 |
| machine-translation | 34718 |
| quote-attribution | 9611 |
| prompt-response | 7645 |
| premise-hypothesis | 7417 |
| question-answer | 2030 |
| dialogue | 1168 |
entity mentions:
| Entity type | Count |
|---|---|
| PERS | 13337 |
| LOC | 3735 |
| GRP | 3020 |
Tagged tokens: 28132
UPOS tags:
| UPOS | Count |
|---|---|
| NOUN | 6916 |
| VERB | 6069 |
| ADJ | 2457 |
| PRON | 2284 |
| ADV | 1973 |
| ADP | 1713 |
| CCONJ | 1649 |
| AUX | 1188 |
| PROPN | 1181 |
| DET | 1155 |
| SCONJ | 721 |
| PART | 615 |
| NUM | 176 |
| INTJ | 34 |
| X | 1 |
Citation
If you use this, please cite:
@misc{mccarthy2026Simulacrum,
author = {McCarthy, Aidan},
title = {{Simulacrum}: Machine Authored and Annotated Latin},
year = {2026},
howpublished = {\url{https://huggingface.co/datasets/aimgo/Simulacrum}},
note = {Dataset}
}
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