5 papers
Quantum Incremental Learning with Mixed State Prototypes
Yu Wu, Qianli Zhou, Xinyang Deng +3
Incremental learning models are required to learn new classes sequentially without catastrophic forgetting, while operating under parameter and memory constraints. In the Noisy Int…
DataRx: Missingness-Aware Sampling for Safer Large Language Model Task-Specific Fine-Tuning
Junbo Zhang, Qianli Zhou, Xinyang Deng +1
Task-specific fine-tuning can improve the performance of large language models (LLMs) on downstream tasks. However, our study reveals that task-specific fine-tuning can also weaken…
DataShield: Safety-degrading Data Filtering for LLM Benign Instruction Fine-Tuning
Junbo Zhang, Qianli Zhou, Xinyang Deng +3
Large language models (LLMs) suffer from degraded safety capabilities even when fine-tuned with benign datasets. However, existing methods for identifying safety-degrading samples…
Feature Entanglement-based Quantum Multimodal Fusion Neural Network
Yu Wu, Qianli Zhou, Jie Geng +2
Multimodal learning aims to enhance perceptual and decision-making capabilities by integrating information from diverse sources. However, classical deep learning approaches face a…
Understanding and Mitigating Over-refusal for Large Language Models via Representation Intervention
Junbo Zhang, Ran Chen, Qianli Zhou +2
Large language models (LLMs) demonstrate powerful capabilities across various natural language processing tasks,yet their inherent safety vulnerabilities undermine the reliable app…