24 citations · 75 across the 22 of their papers we have counts for
9 papers · 1 filter
The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective
Zhen Qin, Daoyuan Chen, Wenhao Zhang +5
The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a br…
Safety Layers in Aligned Large Language Models: The Key to LLM Security
Shen Li, Liuyi Yao, Lan Zhang +1
Aligned LLMs are secure, capable of recognizing and refusing to answer malicious questions. However, the role of internal parameters in maintaining such security is not well unders…
When to Trust LLMs: Aligning Confidence with Response Quality
Shuchang Tao, Liuyi Yao, Hanxing Ding +6
Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlig…
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning
Yue Cui, Liuyi Yao, Zitao Li +3
Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the…
An Auction-based Marketplace for Model Trading in Federated Learning
Yue Cui, Liuyi Yao, Yaliang Li +3
Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. However, the proper valuation of shared data in this collabor…
Double-I Watermark: Protecting Model Copyright for LLM Fine-tuning
Shen Li, Liuyi Yao, Jinyang Gao +2
To support various applications, a prevalent and efficient approach for business owners is leveraging their valuable datasets to fine-tune a pre-trained LLM through the API provide…
AgentScope: A Flexible yet Robust Multi-Agent Platform
Dawei Gao, Zitao Li, Xuchen Pan +16
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooper…
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Jiamu Bai, Daoyuan Chen, Bingchen Qian +2
Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs). While promising, it raises significant challenges due to t…
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
Zhenqing Ling, Daoyuan Chen, Liuyi Yao +2
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing. However, the intensive memory…