6 citations · 9 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios
Zetian Ouyang, Yishuai Qiu, Linlin Wang +4
With the proliferation of Large Language Models (LLMs) in diverse domains, there is a particular need for unified evaluation standards in clinical medical scenarios, where models n…
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…
How Do Humans Write Code? Large Models Do It the Same Way Too
Long Li, Xuzheng He, Haozhe Wang +2
Program-of-Thought (PoT) replaces natural language-based Chain-of-Thought (CoT) as the most popular method in Large Language Models (LLMs) mathematical reasoning tasks by utilizing…