10 papers
Unified Defense for Large Language Models against Jailbreak and Fine-Tuning Attacks in Education
Xin Yi, Yue Li, Dongsheng Shi +3
Large Language Models (LLMs) are increasingly integrated into educational applications. However, they remain vulnerable to jailbreak and fine-tuning attacks, which can compromise s…
AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation
Xiechi Zhang, Zetian Ouyang, Linlin Wang +6
With the proliferation of large language models (LLMs) in the medical domain, there is increasing demand for improved evaluation techniques to assess their capabilities. However, t…
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models
Yue Li, Xin Yi, Dongsheng Shi +3
With the increasing size of Large Vision-Language Models (LVLMs), network pruning techniques aimed at compressing models for deployment in resource-constrained environments have ga…
Unified attacks to large language model watermarks: spoofing and scrubbing in unauthorized knowledge distillation
Xin Yi, Yue Li, Shunfan Zheng +3
Watermarking has emerged as a critical technique for combating misinformation and protecting intellectual property in large language models (LLMs). A recent discovery, termed water…
Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation
Shunfan Zheng, Xiechi Zhang, Gerard de Melo +2
In the rapidly evolving landscape of large language models (LLMs) for medical applications, ensuring the reliability and accuracy of these models in clinical settings is paramount.…
Latent-space adversarial training with post-aware calibration for defending large language models against jailbreak attacks
Xin Yi, Yue Li, Dongsheng Shi +3
Ensuring safety alignment is a critical requirement for large language models (LLMs), particularly given increasing deployment in real-world applications. Despite considerable adva…
NLSR: Neuron-Level Safety Realignment of Large Language Models Against Harmful Fine-Tuning
Xin Yi, Shunfan Zheng, Linlin Wang +3
The emergence of finetuning-as-a-service has revealed a new vulnerability in large language models (LLMs). A mere handful of malicious data uploaded by users can subtly manipulate…
ACE-: Automatic Capability Evaluator for Multimodal Medical Models
Xiechi Zhang, Shunfan Zheng, Linlin Wang +4
As multimodal large language models (MLLMs) gain prominence in the medical field, the need for precise evaluation methods to assess their effectiveness has become critical. While b…
A safety realignment framework via subspace-oriented model fusion for large language models
Xin Yi, Shunfan Zheng, Linlin Wang +2
The current safeguard mechanisms for large language models (LLMs) are indeed susceptible to jailbreak attacks, making them inherently fragile. Even the process of fine-tuning on ap…
Fine-Grained Detoxification via Instance-Level Prefixes for Large Language Models
Xin Yi, Linlin Wang, Xiaoling Wang +1
Impressive results have been achieved in natural language processing (NLP) tasks through the training of large language models (LLMs). However, these models occasionally produce to…