3 citations · 7 across the 5 of their papers we have counts for
5 papers
Understanding Evaluation Illusion in Diffusion Large Language Models
Hengxiang Zhang, Jiaxi Ren, Renchunzi Xie +1
Despite the capability of parallel decoding, diffusion large language models (dLLMs) require many denoising steps to maintain generation quality, motivating recent research on effi…
Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent Improvement
Aaditya Shukla, Sidney Knowles, Meenakshi Madugula +9
Enterprise AI agents must continuously adapt to maintain accuracy, reduce latency, and remain aligned with user needs. We present a practical implementation of a data flywheel in N…
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models
Ji Liu, Jiaxiang Ren, Ruoming Jin +4
As a promising paradigm to collaboratively train models with decentralized data, Federated Learning (FL) can be exploited to fine-tune Large Language Models (LLMs). While LLMs corr…
FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-aware Model Update
Ji Liu, Juncheng Jia, Tianshi Che +5
As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting th…
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
Tianshi Che, Ji Liu, Yang Zhou +5
Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) genera…