Massive Editing for Large Language Models Based on Dynamic Weight Generation
Wentao Wan · Qiqing Lao · Zhiwei Xie · Hefeng Wu · Runnan Lin · Liang Lin · Keze Wang
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Wentao Wan · Qiqing Lao · Zhiwei Xie · Hefeng Wu · Runnan Lin · Liang Lin · Keze Wang
Likui Zhang · Tao Tang · Zhihao Zhan · Xiuwei Chen · Zisheng Chen · Jianhua Han · Jiangtong Zhu · Pei Xu · Hang Xu · Hefeng Wu · Liang Lin · Xiaodan Liang
Cong Wan · Ying He · Zhongzhan Huang · Hefeng Wu
Test-time Scaling (TTS) has emerged as a pivotal research direction for enhancing model performance by dynamically allocating computational resources during inference. Recent advancements have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal re...
Keyang Zhong · Junlin Xie · Hefeng Wu · Haofeng Li · Guanbin Li
Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multi-player game settings with imperfect and deceptive information. In this paper, we pick up a representative multi-player task, Murder Mystery Games, whic...
Guoming Ling · Zhongzhan Huang · Yupei Lin · Junxin Li · Shanshan Zhong · Hefeng Wu · Liang Lin
Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introd...