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论文提出 ForgePrint（搜索-蒸馏框架），通过定向改写将一个模型的输出伪装成目标模型的指纹，使归因分类器误判来源模型，在 CNN/DM 上达到 70.2% 的目标成功率，说明文本归因不能等同于来源真实性。
This paper analyzes how different fine-tuning strategies (FFT, LoRA, quantized LoRA) and model scales affect LLM interpretive behavior for automated code compliance tasks using perturbation-based attribution analysis. The findings show FFT produces more focused attribution patterns than parameter-efficient methods, and larger models develop specific interpretive strategies with diminishing performance returns beyond 7B parameters.