---
title: "FMISUYotkovaKastreva at SemEval-2026 Task 13: Lightweight"
description: "This paper presents a SemEval-2026 Task 13 Subtask A system that labels code snippets as… 95.10% accuracy. 18 references."
authors: ["Elitsa Yotkova", "Violeta Kastreva", "Dimitar Dimitrov", "Ivan Koychev", "Preslav Nakov"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.275/"
doi: "10.18653/v1/2026.semeval-1.275"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yotkova-2026-fmisuyotkovakastreva-semeval-2026-task-13/"
---
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# FMISUYotkovaKastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals

**Authors:** Elitsa Yotkova, Violeta Kastreva, [Dimitar Dimitrov](https://md.research.elamur.ai/free/en/authors/dimitar-iliyanov-dimitrov/), [Ivan Koychev](https://md.research.elamur.ai/free/en/authors/ivan-koychev/) and [Preslav Nakov](https://md.research.elamur.ai/free/en/authors/preslav-nakov/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.275/) · [PDF](https://aclanthology.org/2026.semeval-1.275.pdf) · [DOI 10.18653/v1/2026.semeval-1.275](https://doi.org/10.18653/v1/2026.semeval-1.275) · [OpenAlex](https://openalex.org/W7166844782)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This paper presents a SemEval-2026 Task 13 Subtask A system that labels code snippets as human-written or LLM-generated. The setting is hard because the test data include programming languages and application domains never seen in training. The authors chose not to depend on large pretrained code encoders, which they found brittle under distribution shift. Instead, they built a cheap, interpretable pipeline from length-normalized stylometric ratios, mainly how much of a snippet is comments and how often verbs appear in those comments. Several helper components support feature extraction: Tree-Sitter for parsing, spaCy for spotting verbs, a programming-language identifier, and a line-level classifier that separates code from natural-language text. Pairing a depth-2 decision tree with heuristics derived from data analysis yielded a score of 67.35 in macro-F1 on the organizers' official evaluation set, which put the system within the top 15% of submissions. Training needs only a CPU and inference is almost instant. The work shows that simple, explainable signals can compete with heavy neural detectors.

## Key points
- **Data:** The training set has 500K snippets (238K human, 262K machine), about 457K of them in Python, written in C++, Python and Java. The test set adds Go, PHP, C#, C and JavaScript, plus research and production code.
- **Language identification:** The language identifier was trained on Rosetta Code using character 3–8-gram TF.IDF features and Multinomial Naïve Bayes. It scored 95.10% accuracy on 1K test samples labeled with ChatGPT-5.2, with C/C++ and C#/Java as the main confusions.
- **Code-vs-text classifier:** Built from Stack Overflow code and Twitch chat text, it uses character 3–5-gram TF.IDF features and a linear model. It reached 96.07 macro-F1 on validation and 86.43 on 30 manually annotated test samples.
- **Heuristics:** One heuristic flags a standalone language-name line, a Markdown leak seen in about 5K samples, which added 0.05 macro-F1 over an all-human baseline. Another applies a text-like-ratio threshold of 0.3 and keeps 99.9% specificity on human code.
- **Classifier comparison:** Logistic regression scored 63.16 macro-F1 (64.59 with threshold 0.65), and the depth-2 decision tree scored 65.62 (67.35 with heuristics). Features such as cyclomatic complexity, completeness score, and Statements-to-LOC contributed little.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 138 works · 6,590 citations
- [Sofia University "St. Kliment Ohridski"](https://md.research.elamur.ai/free/en/universities/sofia-university-st-kliment-ohridski/) — University, Sofia, Bulgaria · 23 works · 2,694 citations

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## Cite

Cite as: Elitsa Yotkova, Violeta Kastreva, Dimitar Dimitrov, Ivan Koychev and Preslav Nakov. 2026. FMISUYotkovaKastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yotkova2026fmisuyotkovakastreva,
  title = {{FMISUYotkovaKastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals}},
  author = {Elitsa Yotkova, Violeta Kastreva, Dimitar Dimitrov, Ivan Koychev and Preslav Nakov},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.275},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.275}
}
```

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## References

18 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*