6 papers
Fast and Accurate: Improving Personalized LLM Performance by Semantic Concentration Inspired Workload Split between RAG and Fine-Tuning
Xingke Yang, Sicong Li, Lingfeng Yao +4
With ever increasing personalized large language model (LLM) services, the next question to answer is how to improve the quality of services/users' experiences, which is indicated…
Mind Your Own Role: Reducing Role Overstepping for Effective and Efficient Multi-Agent Collaboration via Dynamic Role Activation Steering
Xingke Yang, Sicong Li, Lingfeng Yao +5
Multi-Agent Systems (MAS) have shown strong capability in solving complex tasks by assigning different agents specialized roles and corresponding subtasks. However, agents may over…
PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation
Lingfeng Yao, Chenpei Huang, Xingke Yang +5
Text-to-spatial audio generation, such as text-to-First-Order Ambisonics (FOA), provides a convenient way to create spatial audio for billion-dollar gaming and film industries. How…
Fed MobiLLM: Efficient Federated LLM Fine-Tuning over Heterogeneous Mobile Devices via Server Assisted Side-Tuning
Xingke Yang, Liang Li, Sicong Li +6
Collaboratively fine-tuning (FT) large language models (LLMs) over heterogeneous mobile devices fosters immense potential applications of personalized intelligence. However, such a…
PAE MobiLLM: Privacy-Aware and Efficient LLM Fine-Tuning on the Mobile Device via Additive Side-Tuning
Xingke Yang, Liang Li, Zhiyi Wan +6
There is a huge gap between numerous intriguing applications fostered by on-device large language model (LLM) fine-tuning (FT) from fresh mobile data and the limited resources of a…
MobiLLM: Enabling LLM Fine-Tuning on the Mobile Device via Server Assisted Side Tuning
Liang Li, Xingke Yang, Wen Wu +5
Large Language Model (LLM) at mobile devices and its potential applications never fail to fascinate. However, on-device LLM fine-tuning poses great challenges due to extremely high…