1 citations · 1 across the 4 of their papers we have counts for
4 papers
Let's Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models
Kangyang Luo, Zichen Ding, Zhenmin Weng +5
While Chain of Thought (CoT) prompting approaches have significantly consolidated the reasoning capabilities of large language models (LLMs), they still face limitations that requi…
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning
Kangyang Luo, Shuai Wang, Yexuan Fu +5
Federated Learning (FL) is a distributed machine learning scheme in which clients jointly participate in the collaborative training of a global model by sharing model information r…
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator
Kangyang Luo, Shuai Wang, Xiang Li +3
Federated Learning (FL) is gaining popularity as a distributed learning framework that only shares model parameters or gradient updates and keeps private data locally. However, FL…
An LLM-Enhanced Adversarial Editing System for Lexical Simplification
Keren Tan, Kangyang Luo, Yunshi Lan +2
Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. I…