2 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
LLM DNA: Tracing Model Evolution via Functional Representations
Zhaomin Wu, Haodong Zhao, Ziyang Wang +3
The explosive growth of large language models (LLMs) has created a vast but opaque landscape: millions of models exist, yet their evolutionary relationships through fine-tuning, di…
Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion
Haodong Zhao, Jidong Li, Zhaomin Wu +4
Understanding persuasion is critical for the safety and reliability of multi-agent systems built on large language models (LLMs). This paper studies persuasion dynamics by contrast…
Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts
Zhaomin Wu, Mingzhe Du, See-Kiong Ng +1
Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A significant and underexplored risk is i…
WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos
Zhaomin Wu, Ziyang Wang, Bingsheng He
Relational databases are often fragmented across organizations, creating data silos that hinder distributed data management and mining. Collaborative learning (CL) -- techniques th…
Learning Relational Tabular Data without Shared Features
Zhaomin Wu, Shida Wang, Ziyang Wang +1
Learning relational tabular data has gained significant attention recently, but most studies focus on single tables, overlooking the potential of cross-table learning. Cross-table…
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
Zhaomin Wu, Zhen Qin, Junyi Hou +4
Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning…