3 citations · 3 across the 13 of their papers we have counts for
13 papers
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…
MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
Huawei Lin, Peng Li, Jie Song +2
Large language model (LLM) agents rely on reusable skills to solve complex tasks, but existing skill creation approaches often treat skills as isolated, static artifacts, limiting…
Randomized Antipodal Search Done Right for Data Pareto Improvement of LLM Unlearning
Ziwen Liu, Huawei Lin, Yide Ran +5
Large language models (LLMs) sometimes memorize undesirable knowledge, which must be removed after deployment. Prior work on machine unlearning has focused largely on optimization…
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…
Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything
Huawei Lin, Yunzhi Shi, Tong Geng +3
Multimodal large language models (MLLMs) have shown strong capabilities but remain limited to fixed modality pairs and require costly fine-tuning with large aligned datasets. Build…
VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation
Huawei Lin, Tong Geng, Tony Geng +2
Autoregressive (AR) models have recently shown strong performance in image generation, where a critical component is the visual tokenizer (VT) that maps continuous pixel inputs to…
ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Party LLM Data Valuation
Yanzhou Pan, Huawei Lin, Yide Ran +5
Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance, especially when working within a limited bu…
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…
Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data
Huawei Lin, Jun Woo Chung, Yingjie Lao +1
Gradient Boosting Decision Tree (GBDT) is one of the most popular machine learning models in various applications. However, in the traditional settings, all data should be simultan…
DMin: Scalable Training Data Influence Estimation for Diffusion Models
Huawei Lin, Yingjie Lao, Weijie Zhao
Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models (DMs), yet existing influence estimation methods ar…
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…
Activation Template Matching Loss for Explainable Face Recognition
Huawei Lin, Haozhe Liu, Qiufu Li +1
Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionals…
Research on Gender-related Fingerprint Features
Yong Qi, Yanping Li, Huawei Lin +2
Fingerprint is an important biological feature of human body, which contains abundant gender information. At present, the academic research of fingerprint gender characteristics is…