1 citations · 1 across the 13 of their papers we have counts for
13 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…
Aligning Large Vision-Language Models at Test Time: A Trajectory-Guided Structured Sampling Approach
Tianbao Jiang, Weicong Ni, Gerard de Melo +1
Post-training reinforcement learning (RL) algorithms are commonly used to align large vision-language models (LVLMs) with human intent and the requirements of visual reasoning task…
SURGENT: A Surgical Multi-Agent Assistance System Across the Perioperative Workflow
Dongsheng Shi, Yue Li, Xin Yi +3
The intricate nature of modern surgical care necessitates intelligent systems that can synthesize extensive patient records, support collaborative decision-making, and provide tran…
AGMark: Attention-Guided Dynamic Watermarking for Large Vision-Language Models
Yue Li, Xin Yi, Dongsheng Shi +3
Watermarking has emerged as a pivotal solution for content traceability and intellectual property protection in large vision language models (LVLMs). However, vision-agnostic water…
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…
From Construction to Injection: Edit-Based Fingerprints for Large Language Models
Yue Li, Xin Yi, Dongsheng Shi +3
Reliable model fingerprints are essential for protecting large language models (LLMs) against unauthorized redistribution and commercial misuse. In black-box deployment, verificati…
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.…
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…