1 citations · 1 across the 4 of their papers we have counts for
12 papers
ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data
Haodong Zhao, Jinming Hu, Zhaomin Wu +7
Federated Instruction Tuning (FIT) enables collaborative instruction tuning of large language models across multiple organizations (clients) in a cross-silo setting without requiri…
Reinforcement Fine-Tuning for History-Aware Dense Retriever in RAG
Yicheng Zhang, Zhen Qin, Zhaomin Wu +2
Retrieval-augmented generation (RAG) enables large language models (LLMs) to produce evidence-based responses, and its performance hinges on the matching between the retriever and…
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…
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…
Mining Intrinsic Rewards from LLM Hidden States for Efficient Best-of-N Sampling
Jizhou Guo, Zhaomin Wu, Hanchen Yang +1
Best-of-N sampling is a powerful method for improving Large Language Model (LLM) performance, but it is often limited by its dependence on massive, text-based reward models. These…