26 papers
Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection
Jun Nie, Yonggang Zhang, Tongliang Liu +3
Robust detection of generated images is critical to counter the misuse of generative models. Existing methods primarily depend on learning from human-annotated training datasets, l…
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…
AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions
Jingwei Sun, Jianing Zhu, Yuanyi Li +3
Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digital workflows. However, real-w…
Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory
Jingwei Sun, Jianing Zhu, Jiangchao Yao +2
To enable reliable long-term interaction, LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history. M…
Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance
Muyang Li, Yucheng Liu, Jianbo Ma +3
Vision-Language Models (VLMs) have enhanced traditional LLMs with visual capabilities through the integration of vision encoders. While recent works have explored various combinati…
Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning
Ali Taheri, Alireza Taban, Qizhou Wang +4
Supervised fine-tuning (SFT) plays a critical role for pretrained large language models (LLMs), notably enhancing their capacity to acquire domain-specific knowledge while preservi…