10 papers
Light Alignment Improves LLM Safety via Model Self-Reflection with a Single Neuron
Sicheng Shen, Mingyang Lv, Han Shen +7
The safety of large language models (LLMs) has increasingly emerged as a fundamental aspect of their development. Existing safety alignment for LLMs is predominantly achieved throu…
CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models
Haibo Tong, Zeyang Yue, Feifei Zhao +6
Whether Large Language Models (LLMs) truly possess human-like Theory of Mind (ToM) capabilities has garnered increasing attention. However, existing benchmarks remain largely restr…
Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Guobin Shen, Dongcheng Zhao, Haibo Tong +3
Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it difficult to obtain effective tr…
Bidirectional Intention Inference Enhances LLMs' Defense Against Multi-Turn Jailbreak Attacks
Haibo Tong, Dongcheng Zhao, Guobin Shen +4
The remarkable capabilities of Large Language Models (LLMs) have raised significant safety concerns, particularly regarding "jailbreak" attacks that exploit adversarial prompts to…
Multi-Level Safety Continual Projection for Fine-Tuned Large Language Models without Retraining
Bing Han, Feifei Zhao, Dongcheng Zhao +4
While fine-tuning services drive the rapid expansion of task capabilities in large language models (LLMs), they are often accompanied by the degradation and reorganization of safet…
C-VARC: A Large-Scale Chinese Value Rule Corpus for Value Alignment of Large Language Models
Ping Wu, Guobin Shen, Dongcheng Zhao +6
Ensuring that Large Language Models (LLMs) align with mainstream human values and ethical norms is crucial for the safe and sustainable development of AI. Current value evaluation…