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    <abstract>This paper describes our system for SemEval-2026 Task 13 Subtask B, which requires attributing source code to either a human author or one of 10 LLM families. Guided by dataset analysis, we identify three practical challenges: formatting fingerprints discarded by tokenizers, heterogeneous code lengths, and extreme class imbalance. We build on unixcoder-base with Explicit Stylistic Prompting, Language-Aware Truncation, and imbalance-aware training (Focal Loss, GeM pooling, multi-sample dropout, and bucket batching). Our system achieves 0.434 Macro F1 on the official hidden test set, ranking 4th out of 34 teams with only 125M parameters. Controlled 5-fold cross-validation confirms that each component contributes to the final system, and a formatting-normalization study quantifies the model’s reliance on formatting cues.</abstract>
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%0 Conference Proceedings
%T SYSUpporter at SemEval-2026 Task 13: Leveraging Stylistic Signals and Language-Aware Truncation for Machine-Generated Code Detection
%A Chen, Longfeng
%A Xiao, Zheng
%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 chen-xiao-2026-sysupporter
%X This paper describes our system for SemEval-2026 Task 13 Subtask B, which requires attributing source code to either a human author or one of 10 LLM families. Guided by dataset analysis, we identify three practical challenges: formatting fingerprints discarded by tokenizers, heterogeneous code lengths, and extreme class imbalance. We build on unixcoder-base with Explicit Stylistic Prompting, Language-Aware Truncation, and imbalance-aware training (Focal Loss, GeM pooling, multi-sample dropout, and bucket batching). Our system achieves 0.434 Macro F1 on the official hidden test set, ranking 4th out of 34 teams with only 125M parameters. Controlled 5-fold cross-validation confirms that each component contributes to the final system, and a formatting-normalization study quantifies the model’s reliance on formatting cues.
%U https://aclanthology.org/2026.semeval-1.364/
%P 2903-2909

Markdown (Informal)

[SYSUpporter at SemEval-2026 Task 13: Leveraging Stylistic Signals and Language-Aware Truncation for Machine-Generated Code Detection](https://aclanthology.org/2026.semeval-1.364/) (Chen & Xiao, SemEval 2026)

• SYSUpporter at SemEval-2026 Task 13: Leveraging Stylistic Signals and Language-Aware Truncation for Machine-Generated Code Detection (Chen & Xiao, SemEval 2026)

ACL

• Longfeng Chen and Zheng Xiao. 2026. SYSUpporter at SemEval-2026 Task 13: Leveraging Stylistic Signals and Language-Aware Truncation for Machine-Generated Code Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2903–2909, San Diego, California, USA. Association for Computational Linguistics.