@inproceedings{d-etal-2026-bitzkrieg,
title = "Bitzkrieg at {S}em{E}val-2026 Task 13: Calibration-Aware Dual {C}ode{BERT} for Multilingual Machine-Generated Code Detection",
author = "D., Thenmozhi and
S, Adithya and
Malisetty, Harshil and
P, Aadit and
R, Rohan",
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-1.282/",
pages = "2233--2237",
ISBN = "979-8-89176-414-9",
abstract = "We describe our submission to SemEval-2026 Task 13, addressing binary detection (Subtask A), generator attribution (Subtask B), and hybrid/adversarial authorship classification (Subtask C) of machine-generated code (MGC). For Subtask A, we fine-tune two CodeBERT models with complementary sampling strategies and apply percentile-based post-hoc calibration, improving Macro-F1 from 0.47 to 0.56 without additional training. For Subtask B, we combine TF-IDF n-grams, frozen CodeBERT embeddings, and language features with XGBoost, using synthetic augmentation and class weighting to handle an 11-class dataset skewed 88{\%} toward the human class, achieving Macro-F1 of 0.289. For Subtask C, we fine-tune a CodeBERT classifier for four-way authorship classification, achieving Macro-F1 of 0.49. Our results highlight the importance of probability calibration for binary detection and class balancing for multi-class attribution."
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<abstract>We describe our submission to SemEval-2026 Task 13, addressing binary detection (Subtask A), generator attribution (Subtask B), and hybrid/adversarial authorship classification (Subtask C) of machine-generated code (MGC). For Subtask A, we fine-tune two CodeBERT models with complementary sampling strategies and apply percentile-based post-hoc calibration, improving Macro-F1 from 0.47 to 0.56 without additional training. For Subtask B, we combine TF-IDF n-grams, frozen CodeBERT embeddings, and language features with XGBoost, using synthetic augmentation and class weighting to handle an 11-class dataset skewed 88% toward the human class, achieving Macro-F1 of 0.289. For Subtask C, we fine-tune a CodeBERT classifier for four-way authorship classification, achieving Macro-F1 of 0.49. Our results highlight the importance of probability calibration for binary detection and class balancing for multi-class attribution.</abstract>
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%0 Conference Proceedings
%T Bitzkrieg at SemEval-2026 Task 13: Calibration-Aware Dual CodeBERT for Multilingual Machine-Generated Code Detection
%A D., Thenmozhi
%A S, Adithya
%A Malisetty, Harshil
%A P, Aadit
%A R, Rohan
%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 d-etal-2026-bitzkrieg
%X We describe our submission to SemEval-2026 Task 13, addressing binary detection (Subtask A), generator attribution (Subtask B), and hybrid/adversarial authorship classification (Subtask C) of machine-generated code (MGC). For Subtask A, we fine-tune two CodeBERT models with complementary sampling strategies and apply percentile-based post-hoc calibration, improving Macro-F1 from 0.47 to 0.56 without additional training. For Subtask B, we combine TF-IDF n-grams, frozen CodeBERT embeddings, and language features with XGBoost, using synthetic augmentation and class weighting to handle an 11-class dataset skewed 88% toward the human class, achieving Macro-F1 of 0.289. For Subtask C, we fine-tune a CodeBERT classifier for four-way authorship classification, achieving Macro-F1 of 0.49. Our results highlight the importance of probability calibration for binary detection and class balancing for multi-class attribution.
%U https://aclanthology.org/2026.semeval-1.282/
%P 2233-2237
Markdown (Informal)
[Bitzkrieg at SemEval-2026 Task 13: Calibration-Aware Dual CodeBERT for Multilingual Machine-Generated Code Detection](https://aclanthology.org/2026.semeval-1.282/) (D. et al., SemEval 2026)
ACL