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This paper presents an iterative imbalance-aware fine-tuning approach using Qwen3-8B with QLoRA for psychological defense mechanism classification, achieving a macro F1 of 0.3917 and ranking 4th out of 21 teams in the PsyDefDetect 2026 shared task.
This paper proposes a context-aware synthetic augmentation framework combined with a hybrid classification model to address data scarcity and class imbalance in classifying psychological defense mechanisms from text. The method achieves significant improvements on the PsyDefDetect shared task benchmark.