Zobrazeno 1 - 10
of 249
pro vyhledávání: '"WEN Lijie"'
Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within images into various languages. However, existing datasets often suffer from limitations in scale, diversity, and quality, hinder
Externí odkaz:
http://arxiv.org/abs/2412.07147
Autor:
Chen, Junzhe, Zhang, Tianshu, Huang, Shiyu, Niu, Yuwei, Zhang, Linfeng, Wen, Lijie, Hu, Xuming
Despite the recent breakthroughs achieved by Large Vision Language Models (LVLMs) in understanding and responding to complex visual-textual contexts, their inherent hallucination tendencies limit their practical application in real-world scenarios th
Externí odkaz:
http://arxiv.org/abs/2411.15268
Trajectory prediction for multi-agents in complex scenarios is crucial for applications like autonomous driving. However, existing methods often overlook environmental biases, which leads to poor generalization. Additionally, hardware constraints lim
Externí odkaz:
http://arxiv.org/abs/2411.12313
Autor:
Liu, Aiwei, Bai, Haoping, Lu, Zhiyun, Sun, Yanchao, Kong, Xiang, Wang, Simon, Shan, Jiulong, Jose, Albin Madappally, Liu, Xiaojiang, Wen, Lijie, Yu, Philip S., Cao, Meng
Direct Preference Optimization (DPO) has been widely adopted for preference alignment of Large Language Models (LLMs) due to its simplicity and effectiveness. However, DPO is derived as a bandit problem in which the whole response is treated as a sin
Externí odkaz:
http://arxiv.org/abs/2410.04350
Autor:
Liu, Aiwei, Guan, Sheng, Liu, Yiming, Pan, Leyi, Zhang, Yifei, Fang, Liancheng, Wen, Lijie, Yu, Philip S., Hu, Xuming
Text watermarking for Large Language Models (LLMs) has made significant progress in detecting LLM outputs and preventing misuse. Current watermarking techniques offer high detectability, minimal impact on text quality, and robustness to text editing.
Externí odkaz:
http://arxiv.org/abs/2410.03168
Autor:
Gao, Zitian, Niu, Boye, He, Xuzheng, Xu, Haotian, Liu, Hongzhang, Liu, Aiwei, Hu, Xuming, Wen, Lijie
We propose SC-MCTS*: a novel Monte Carlo Tree Search (MCTS) reasoning algorithm for Large Language Models (LLMs), significantly improves both reasoning accuracy and speed. Our motivation comes from: 1. Previous MCTS LLM reasoning works often overlook
Externí odkaz:
http://arxiv.org/abs/2410.01707
Autor:
Pan, Leyi, Liu, Aiwei, Lu, Yijian, Gao, Zitian, Di, Yichen, Huang, Shiyu, Wen, Lijie, King, Irwin, Yu, Philip S.
Watermarking algorithms for large language models (LLMs) have attained high accuracy in detecting LLM-generated text. However, existing methods primarily focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world
Externí odkaz:
http://arxiv.org/abs/2409.05112
Driven by the demand for cross-sentence and large-scale relation extraction, document-level relation extraction (DocRE) has attracted increasing research interest. Despite the continuous improvement in performance, we find that existing DocRE models
Externí odkaz:
http://arxiv.org/abs/2406.07444
Autor:
Wu, Xuan, Wang, Di, Wen, Lijie, Xiao, Yubin, Wu, Chunguo, Wu, Yuesong, Yu, Chaoyu, Maskell, Douglas L., Zhou, You
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing surveys did not cover the state-of-the-art (SOTA) NCO solvers emerged recen
Externí odkaz:
http://arxiv.org/abs/2406.00415
Autor:
Pan, Leyi, Liu, Aiwei, He, Zhiwei, Gao, Zitian, Zhao, Xuandong, Lu, Yijian, Zhou, Binglin, Liu, Shuliang, Hu, Xuming, Wen, Lijie, King, Irwin, Yu, Philip S.
LLM watermarking, which embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text, has become crucial in mitigating the potential misuse of large language models. However, the abundance of LLM waterma
Externí odkaz:
http://arxiv.org/abs/2405.10051