Title: On Adversarial Robustness of Language Models in Transfer Learning Show Chinese title

Title: 在迁移学习中的语言模型的对抗性鲁棒性

Abstract: We investigate the adversarial robustness of LLMs in transfer learning scenarios. Through comprehensive experiments on multiple datasets (MBIB Hate Speech, MBIB Political Bias, MBIB Gender Bias) and various model architectures (BERT, RoBERTa, GPT-2, Gemma, Phi), we reveal that transfer learning, while improving standard performance metrics, often leads to increased vulnerability to adversarial attacks. Our findings demonstrate that larger models exhibit greater resilience to this phenomenon, suggesting a complex interplay between model size, architecture, and adaptation methods. Our work highlights the crucial need for considering adversarial robustness in transfer learning scenarios and provides insights into maintaining model security without compromising performance. These findings have significant implications for the development and deployment of LLMs in real-world applications where both performance and robustness are paramount. Show Chinese abstract

Abstract: 我们研究了大型语言模型（LLMs）在迁移学习场景下的对抗鲁棒性。通过在多个数据集（MBIB仇恨言论、MBIB政治偏见、MBIB性别偏见）和多种模型架构（BERT、RoBERTa、GPT-2、Gemma、Phi）上的综合实验，我们揭示出虽然迁移学习能够提升标准性能指标，但往往会导致对抗攻击的脆弱性增加。我们的研究表明，更大的模型在这方面的表现更具韧性，这表明模型大小、架构以及适应方法之间存在复杂的相互作用。我们的工作强调了在迁移学习场景下考虑对抗鲁棒性的必要性，并提供了在不影响性能的情况下保持模型安全性的见解。这些发现对于实际应用中LLMs的开发和部署具有重要意义，在这些应用中，性能和鲁棒性同样至关重要。