@inproceedings{patel-etal-2026-transformertrio,
title = "{T}ransformer{T}rio at {S}em{E}val-2026 Task 13: Navigating Domain Shift and Representation Instability in Machine-Generated Code Detection",
author = "Patel, Avi and
Laddha, Manthan and
Sapovadiya, Pushti and
Mishra, Pruthwik and
Malviya, Shrikant",
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-1.140/",
pages = "1015--1026",
ISBN = "979-8-89176-414-9",
abstract = "Detecting machine-generated code is increasingly challenging due to advances in code generation models and domain variation across programming tasks. We present our submissions to SemEval-2026 Task 13, evaluating detection in three settings: binary human vs. machine classification, multi-class generator attribution, and four-way authorship classification including hybrid and adversarial cases. We compare feature-based, transformer-based, and hybrid approaches under domain shift and limited supervision. Results show that domain-specific signals often dominate model decisions, degrading generalization when training and test distributions diverge. Increasing model complexity does not consistently improve performance in low-resource or cross-domain settings and may amplify spurious correlations. These findings emphasize robustness and feature alignment over model sophistication for reliable detection."
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<abstract>Detecting machine-generated code is increasingly challenging due to advances in code generation models and domain variation across programming tasks. We present our submissions to SemEval-2026 Task 13, evaluating detection in three settings: binary human vs. machine classification, multi-class generator attribution, and four-way authorship classification including hybrid and adversarial cases. We compare feature-based, transformer-based, and hybrid approaches under domain shift and limited supervision. Results show that domain-specific signals often dominate model decisions, degrading generalization when training and test distributions diverge. Increasing model complexity does not consistently improve performance in low-resource or cross-domain settings and may amplify spurious correlations. These findings emphasize robustness and feature alignment over model sophistication for reliable detection.</abstract>
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%0 Conference Proceedings
%T TransformerTrio at SemEval-2026 Task 13: Navigating Domain Shift and Representation Instability in Machine-Generated Code Detection
%A Patel, Avi
%A Laddha, Manthan
%A Sapovadiya, Pushti
%A Mishra, Pruthwik
%A Malviya, Shrikant
%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 patel-etal-2026-transformertrio
%X Detecting machine-generated code is increasingly challenging due to advances in code generation models and domain variation across programming tasks. We present our submissions to SemEval-2026 Task 13, evaluating detection in three settings: binary human vs. machine classification, multi-class generator attribution, and four-way authorship classification including hybrid and adversarial cases. We compare feature-based, transformer-based, and hybrid approaches under domain shift and limited supervision. Results show that domain-specific signals often dominate model decisions, degrading generalization when training and test distributions diverge. Increasing model complexity does not consistently improve performance in low-resource or cross-domain settings and may amplify spurious correlations. These findings emphasize robustness and feature alignment over model sophistication for reliable detection.
%U https://aclanthology.org/2026.semeval-1.140/
%P 1015-1026
Markdown (Informal)
[TransformerTrio at SemEval-2026 Task 13: Navigating Domain Shift and Representation Instability in Machine-Generated Code Detection](https://aclanthology.org/2026.semeval-1.140/) (Patel et al., SemEval 2026)
ACL