TeleAI at SemEval-2026 Task 13: Data-Centric Full-Parameter Fine-Tuning with Multi-Level Ensembling for Generated Code Detection
Year: 2026
Type: conference-paper
Abstract: This paper presents our top-ranking system for SemEval-2026 Task 13 on code generation detection under multi-lingual and distributionshift settings.Our approach achieved 1st place in Subtasks A and B, and 2nd place in Subtask C in the official evaluation.Our framework integrates data-centric analysis, full-parameter model adaptation, and multilevel ensemble learning.We first analyze label and length distributions and apply repeated oversampling to address class imbalance.We then optimize prompts in a data-driven manner to improve inference stability.Based on Qwen3-30B-A3B-Instruct, we conduct fullparameter fine-tuning with diverse training configurations and integrate multiple checkpoints using soft voting, hard voting, logits-based voting, and LightGBM stacking.Experimental results demonstrate substantial improvements over zero-shot baselines and consistent gains from ensemble strategies, validating the effectiveness of systematic adaptation and ensembling for robust code generation detection.
Institutions: China Telecom, China Telecom (China)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166874261