Dataset Inference in Fine-Tuned Large Language Models: A Comparative Study

Abstract

Dataset Inference provides a robust framework for auditing data ownership by aggregating statistical signals that traditional Membership Inference Attacks (MIAs) fail to capture. While proven for LLM pre-training, its efficacy during fine-tuning is largely unexplored. We evaluate Dataset Inference on Gemma, Llama, and Qwen models using Full Fine-Tuning (FFT), LoRA, and QLoRA. Our findings reveal a stark architectural divergence: ParameterEfficient Fine-Tuning (PEFT) mitigates data leakage in Llama (AUC ≈ 0.50, p > 0.05), while Gemma and Qwen remain highly vulnerable across all adaptation methods (p < 0.05). Additionally, higher learning rates accelerate data absorption, and QLoRA provides only marginal regularization compared to standard LoRA.