6 papers
Evaluating LLMs in Database Scenarios: A Lifecycle Benchmark for Assessing Their Potential in Core Database Tasks
Shunfan Zheng, Dongsheng Shi, Yue Li +3
Large Language Models (LLMs) are transforming database interaction paradigms, evolving from simple query translators to autonomous database administrators (DBAs). However, current…
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.…
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…