84 citations · 100 across the 33 of their papers we have counts for
6 papers · 1 filter
DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation
Rui Chu, Bingyin Zhao, Thanh Quoc Hung Le +6
Large language models (LLMs) have achieved unprecedented success due to their exceptional generative capabilities. However, because they depend on knowledge encapsulated from train…
HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models
Guoshenghui Zhao, Tan Yu, Weijie Zhao
Diffusion large language models generate text through iterative denoising, exposing hidden trajectories that may contain reliability signals beyond the final output. We propose HIV…
RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning
Guoshenghui Zhao, Huawei Lin, Weijie Zhao
Machine unlearning for large language models (LLMs) remains challenging because full retraining is costly, while approximate methods often struggle to remove targeted behaviors wit…
UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models
Huawei Lin, Yingjie Lao, Tong Geng +3
Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prompts or models to generate harmful outp…
Measuring Copyright Risks of Large Language Model via Partial Information Probing
Weijie Zhao, Huajie Shao, Zhaozhuo Xu +2
Exploring the data sources used to train Large Language Models (LLMs) is a crucial direction in investigating potential copyright infringement by these models. While this approach…
Token-wise Influential Training Data Retrieval for Large Language Models
Huawei Lin, Jikai Long, Zhaozhuo Xu +1
Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to…