4 papers
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
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…