@inproceedings{maslo-gargova-2025-bust,
title = "{B}u{ST}: A {S}iamese Transformer Model for {AI} Text Detection in {B}ulgarian",
author = "Maslo, Andrii and
Gargova, Silvia",
editor = "Przyby{\l}a, Piotr and
Shardlow, Matthew and
Colombatto, Clara and
Inie, Nanna",
booktitle = "Proceedings of Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.ommm-1.5/",
pages = "45--52",
abstract = "We introduce BuST (Bulgarian Siamese Transformer), a novel method for detecting machine-generated Bulgarian text using paraphrase-based semantic similarity. Inspired by the RAIDAR approach, BuST employs a Siamese Transformer architecture to compare input texts with their LLM-generated paraphrases, identifying subtle linguistic patterns that indicate synthetic origin. In pilot experiments, BuST achieved 88.79{\%} accuracy and an F1-score of 88.0{\%}, performing competitively with strong baselines. While BERT reached higher raw scores, BuST offers a model-agnostic and adaptable framework for low-resource settings, demonstrating the promise of paraphrase-driven detection strategies."
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%0 Conference Proceedings
%T BuST: A Siamese Transformer Model for AI Text Detection in Bulgarian
%A Maslo, Andrii
%A Gargova, Silvia
%Y Przybyła, Piotr
%Y Shardlow, Matthew
%Y Colombatto, Clara
%Y Inie, Nanna
%S Proceedings of Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F maslo-gargova-2025-bust
%X We introduce BuST (Bulgarian Siamese Transformer), a novel method for detecting machine-generated Bulgarian text using paraphrase-based semantic similarity. Inspired by the RAIDAR approach, BuST employs a Siamese Transformer architecture to compare input texts with their LLM-generated paraphrases, identifying subtle linguistic patterns that indicate synthetic origin. In pilot experiments, BuST achieved 88.79% accuracy and an F1-score of 88.0%, performing competitively with strong baselines. While BERT reached higher raw scores, BuST offers a model-agnostic and adaptable framework for low-resource settings, demonstrating the promise of paraphrase-driven detection strategies.
%U https://aclanthology.org/2025.ommm-1.5/
%P 45-52
Markdown (Informal)
[BuST: A Siamese Transformer Model for AI Text Detection in Bulgarian](https://aclanthology.org/2025.ommm-1.5/) (Maslo & Gargova, OMMM 2025)
ACL