5 papers
ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models
Jiahui Guang, Haiyan Wang, Yingjie Zhu +4
Multimodal large language models (MLLMs) may memorize sensitive cross-modal information during pretraining, making machine unlearning (MU) crucial. Existing methods typically evalu…
PPU-Bench:Real World Benchmark for Personalized Partial Unlearning in Vision Language Models
Jiahui Guang, Zexun Zhan, Zhenlin Xu +5
Multimodal Large Language Models (MLLMs) may memorize sensitive cross-modal information during pretraining. However, existing MLLM unlearning benchmarks rely on synthetic knowledge…
When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation
Guodong Fan, Cuiyun Gao, Chun Yong Chong +4
The rapid evolution of software services poses substantial challenges to the design and implementation of effective recommendation systems. Traditional service recommendation appro…
A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
Cuiyun Gao, Guodong Fan, Chun Yong Chong +5
Model hallucination is one of the most critical challenges faced by Large Language Models (LLMs), especially in high-stakes code intelligence tasks. As LLMs become increasingly int…
LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations
Junxiao Han, Yarong Wang, Xiaodong Gu +5
In this paper, we propose LibRec, a novel framework that integrates the capabilities of LLMs with retrieval-augmented generation(RAG) techniques to automate the recommendation of a…