@inproceedings{ali-fatima-2026-nust,
title = "{NUST} {C}ode{I}ntel at {S}em{E}val-2026 Task 13: Cross-Domain Detection of Machine-Generated Code via Stylometric Features and Transformer Models",
author = "Ali, Azher and
Fatima, Mehwish",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.61/",
pages = "426--433",
ISBN = "979-8-89176-414-9",
abstract = "We present our submission to SemEval-2026 Task 13 on cross-language and cross-domain detection of machine-generated code. We compare TF-IDF-based models with stylometric features against LoRA-tuned transformer encoders. While transformers achieve near-perfect in-distribution performance, they degrade sharply on unseen languages and domains. In contrast, a TF-IDF + Logistic Regression model attains the best test Macro-F1 and shows greater robustness. These results highlight the limitations of neural models under distribution shift and the strength of lexical baselines for cross-domain generalization."
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%0 Conference Proceedings
%T NUST CodeIntel at SemEval-2026 Task 13: Cross-Domain Detection of Machine-Generated Code via Stylometric Features and Transformer Models
%A Ali, Azher
%A Fatima, Mehwish
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F ali-fatima-2026-nust
%X We present our submission to SemEval-2026 Task 13 on cross-language and cross-domain detection of machine-generated code. We compare TF-IDF-based models with stylometric features against LoRA-tuned transformer encoders. While transformers achieve near-perfect in-distribution performance, they degrade sharply on unseen languages and domains. In contrast, a TF-IDF + Logistic Regression model attains the best test Macro-F1 and shows greater robustness. These results highlight the limitations of neural models under distribution shift and the strength of lexical baselines for cross-domain generalization.
%U https://aclanthology.org/2026.semeval-1.61/
%P 426-433
Markdown (Informal)
[NUST CodeIntel at SemEval-2026 Task 13: Cross-Domain Detection of Machine-Generated Code via Stylometric Features and Transformer Models](https://aclanthology.org/2026.semeval-1.61/) (Ali & Fatima, SemEval 2026)
ACL