20 citations · 56 across the 24 of their papers we have counts for
7 papers · 1 filter
Understanding Negative Learning in Large Language Model Fine-tuning
Yue Wang, Qizhou Wang, Zizhuo Zhang +3
Negative learning has never been more important than it is today, particularly in large language model (LLM) fine-tuning, where it underpins machine unlearning, preference- and rew…
LLM Unlearning with LLM Beliefs
Kemou Li, Qizhou Wang, Yue Wang +4
Large language models trained on vast corpora inherently risk memorizing sensitive or harmful content, which may later resurface in their outputs. Prevailing unlearning methods gen…
Towards Understanding Valuable Preference Data for Large Language Model Alignment
Zizhuo Zhang, Qizhou Wang, Shanshan Ye +4
Large language model (LLM) alignment is typically achieved through learning from human preference comparisons, making the quality of preference data critical to its success. Existi…
EEPO: Exploration-Enhanced Policy Optimization via Sample-Then-Forget
Liang Chen, Xueting Han, Qizhou Wang +4
Balancing exploration and exploitation remains a central challenge in reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs). Current RLVR methods o…
Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning
Puning Yang, Qizhou Wang, Zhuo Huang +3
Loss reweighting has shown significant benefits for machine unlearning with large language models (LLMs). However, their exact functionalities are left unclear and the optimal stra…
GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs
Yue Wang, Qizhou Wang, Feng Liu +4
Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. Howeve…
Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond
Qizhou Wang, Jin Peng Zhou, Zhanke Zhou +3
Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are cr…