Lei Yu

Yuetian Chen, Zhiqi Wang, Nathalie Baracaldo et al.

Membership inference attacks (MIAs) pose a critical threat to the privacy of training data in deep learning. Despite significant progress in attack methodologies, our understanding of when and how models encode membership information during training remains limited. This paper presents a dynamic analytical framework for dissecting and quantifying privacy leakage dynamics at the individual sample level. By tracking per-sample vulnerabilities on an FPR-TPR plane throughout training, our framework systematically measures how factors such as dataset complexity, model architecture, and optimizer choice influence the rate and severity at which samples become vulnerable. Crucially, we discover a robust correlation between a sample's intrinsic learning difficulty, and find that the privacy risk of samples highly vulnerable in the final trained model is largely determined early during training. Our results thus provide a deeper understanding of how privacy risks dynamically emerge during training, laying the groundwork for proactive, privacy-aware model training strategies.

Yu Lei, Zixuan Wang, Chu Liu et al.

Large Language Models (LLMs) have demonstrated strong performance across various general Natural Language Processing (NLP) tasks. However, their effectiveness in financial credit assessment applications remains suboptimal, primarily due to the specialized financial expertise required for these tasks. To address this limitation, we propose ZiGong, a Mistral-based model enhanced through multi-task supervised fine-tuning. To specifically combat model hallucination in financial contexts, we introduce a novel data pruning methodology. Our approach utilizes a proxy model to score training samples, subsequently combining filtered data with original datasets for model training. This data refinement strategy effectively reduces hallucinations in LLMs while maintaining reliability in downstream financial applications. Experimental results show our method significantly enhances model robustness and prediction accuracy in real-world financial scenarios.