4 papers
JailbreakOPT: Tool-Assisted Iterative Jailbreak Prompt Optimization
Ge Shi, Jun Yin, Donglin Xie +3
Jailbreak attacks expose persistent safety weaknesses in large language models (LLMs), but existing stateless single-turn methods face a trade-off: hand-crafted prompts are express…
MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service
Timothy Tin Long Yu, Gursimran Singh, Ge Shi +3
Reinforcement Learning from Verifiable Rewards (RLVR) has significantly improved the reasoning capabilities of large language models (LLMs), particularly in multi-turn agentic sett…
ExpertWeave: Efficiently Serving Expert-Specialized Fine-Tuned Adapters at Scale
Ge Shi, Hanieh Sadri, Qian Wang +4
Expert-Specialized Fine-Tuning (ESFT) adapts Mixture-of-Experts (MoE) large language models to enhance their task-specific performance by selectively tuning the top-activated exper…
Enhancing Learned Knowledge in LoRA Adapters Through Efficient Contrastive Decoding on Ascend NPUs
Morgan Lindsay Heisler, Linzi Xing, Ge Shi +7
Huawei Cloud users leverage LoRA (Low-Rank Adaptation) as an efficient and scalable method to fine-tune and customize large language models (LLMs) for application-specific needs. H…