15 papers
MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
He Li, Haoang Chi, Qizhou Wang +6
Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific co…
Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
Puning Yang, Junchi Yu, Qizhou Wang +3
Mitigating sensitive and harmful outputs is fundamental to ensuring safe deployment of LLMs. Existing approaches typically follow two paradigms: Knowledge Deletion (KD), which eras…
What Is Preference Optimization Doing, and Why?
Yue Wang, Qizhou Wang, Zizhuo Zhang +3
Preference optimization (PO) is indispensable for large language models (LLMs), with methods such as direct preference optimization (DPO) and proximal policy optimization (PPO) ach…
Belief Memory: Agent Memory Under Partial Observability
Junfeng Liao, Qizhou Wang, Jianing Zhu +3
LLM agents that operate over long context depend on external memory to accumulate knowledge over time. However, existing methods typically store each observation as a single determ…
Graph Federated Unlearning for Privacy Preservation
Ruotong Ma, Wentao Yu, Qizhou Wang +2
Graph federated learning (GFL) facilitates decentralized training on distributed graph data while keeping sensitive user information local, aligning with policies such as GDPR and…
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…