11 papers
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…
Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation
Yide Ran, Jianwen Xie, Minghui Wang +4
Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods suffer from scalability chall…
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks
Guangwei Zhang, Jianing Zhu, Cheng Qian +12
We present Copyright Detective, the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. The system treats copyrigh…
Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts
Rushi Wang, Jiateng Liu, Cheng Qian +6
Incorporating external context can significantly enhance the response quality of Large Language Models (LLMs). However, real-world contexts often mix relevant information with disp…
Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity
Yide Ran, Wentao Guo, Jingwei Sun +7
Federated Learning enables collaborative fine-tuning of Large Language Models (LLMs) across decentralized Non-Independent and Identically Distributed (Non-IID) clients, but such mo…
VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation
Huawei Lin, Tong Geng, Zhaozhuo Xu +1
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…