20 citations · 56 across the 24 of their papers we have counts for
11 papers · 1 filter
Refuse the Harm, Complete the Task via Graph-of-Rules Planning for LLM-Enabled Robots
Ali Taheri, Alireza Taban, Qizhou Wang +1
Large Language Models (LLMs) are increasingly used in embodied AI for robotic manipulation and planning, but their deployment in physical systems exposes vulnerabilities such as ja…
Encoding Context into LLM Weights: What Makes It Context?
Yue Wang, Qizhou Wang, Feng Liu +3
Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with context length. An emerging alter…
LLM Persona Unlearning
Kemou Li, Zhuan Shi, Qizhou Wang +4
Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-training teaches conditional…
Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing
Kemou Li, Qizhou Wang, Yue Wang +6
Open-weight LLMs are released not only as fixed products but also as substrates for downstream fine-tuning. This openness, however, creates legal and ethical risks because users ma…
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…
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…
AEGIS: Adversarial Target-Guided Retention-Data-Free Robust Concept Erasure from Diffusion Models
Fengpeng Li, Kemou Li, Qizhou Wang +2
Concept erasure helps stop diffusion models (DMs) from generating harmful content; but current methods face robustness retention trade off. Robustness means the model fine-tuned by…
Explainable LLM Unlearning Through Reasoning
Junfeng Liao, Qizhou Wang, Shanshan Ye +3
LLM unlearning is essential for mitigating safety, copyright, and privacy concerns in pre-trained large language models (LLMs). Compared to preference alignment, it offers a more e…
Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning
Zhuo Huang, Qizhou Wang, Ziming Hong +3
For ethical and safe AI, machine unlearning rises as a critical topic aiming to protect sensitive, private, and copyrighted knowledge from misuse. To achieve this goal, it is commo…