6 papers
CORTEX: Learning to Share and Specialize in Dense Language Models
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Large language models are trained on heterogeneous data mixtures, where different knowledge domains require both shared knowledge and specialization. Existing modular approaches ty…
Social Circuits behind Multi-agent Echo Chambers
Chuiyang Meng, Wenlu Yu, Ming Tang +1
Language-model agents exchange messages to combine evidence, but their communication can also create echo chambers that reinforce shared errors. However, overall task performance d…
Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning
Jiahao Zeng, Ming Tang, Ningning Ding
Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense. LLM routing aims to mitigate expenses while maintai…
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the…
ZorBA: Zeroth-order Federated Fine-tuning of LLMs with Heterogeneous Block Activation
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
Federated fine-tuning of large language models (LLMs) enables collaborative tuning across distributed clients. However, due to the large size of LLMs, local updates in federated le…
Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing
Ning Yang, Junrui Wen, Meng Zhang +1
Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy efficiency. Howeve…