Contrastive Knowledge Transfer and Robust Optimization for Secure Alignment of Large Language Models

2025-10-30

AI Summary

To address the dual limitations of large language models (LLMs) in safety alignment and out-of-distribution robustness, this paper proposes a frozen-backbone contrastive distillation–noise-robust joint fine-tuning framework. The method unifies contrastive knowledge distillation—preserving the teacher model’s semantic decision boundaries—with noise-robust optimization—introducing input perturbations and imposing gradient constraints—within a single objective function comprising knowledge distillation loss, robustness loss, and L2 regularization. It supports mixed-precision training and multi-budget adaptation. Experiments demonstrate that our approach consistently surpasses state-of-the-art baselines across alignment accuracy, adversarial/natural perturbation robustness, and safety metrics, achieving SOTA performance on multiple benchmarks. Moreover, it significantly enhances knowledge transfer efficiency and deployment reliability.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This paper addresses the limitations of large-scale language models in safety alignment and robustness by proposing a fine-tuning method that combines contrastive distillation with noise-robust training. The method freezes the backbone model and transfers the knowledge boundaries of the teacher model to the student model through distillation, thereby improving semantic consistency and alignment accuracy. At the same time, noise perturbations and robust optimization constraints are introduced during training to ensure that the model maintains stable predictive outputs under noisy and uncertain inputs. The overall framework consists of distillation loss, robustness loss, and a regularization term, forming a unified optimization objective that balances alignment ability with resistance to interference. To systematically validate its effectiveness, the study designs experiments from multiple perspectives, including distillation weight sensitivity, stability analysis under computation budgets and mixed-precision environments, and the impact of data noise and distribution shifts on model performance. Results show that the method significantly outperforms existing baselines in knowledge transfer, robustness, and overall safety, achieving the best performance across several key metrics. This work not only enriches the theoretical system of parameter-efficient fine-tuning but also provides a new solution for building safer and more trustworthy alignment mechanisms.

Problem

• Enhancing safety alignment and robustness in large language models
• Improving semantic consistency through contrastive knowledge distillation
• Maintaining stable predictions under noisy inputs via robust optimization

Innovation

• Contrastive distillation transfers teacher knowledge boundaries
• Noise perturbations enhance model robustness during training
• Unified optimization combines distillation and robustness losses