Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Wen, Lijie"'
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, 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
Multi-modal reasoning plays a vital role in bridging the gap between textual and visual information, enabling a deeper understanding of the context. This paper presents the Feature Swapping Multi-modal Reasoning (FSMR) model, designed to enhance mult
Externí odkaz:
http://arxiv.org/abs/2403.20026
The literature review is an indispensable step in the research process. It provides the benefit of comprehending the research problem and understanding the current research situation while conducting a comparative analysis of prior works. However, li
Externí odkaz:
http://arxiv.org/abs/2403.02574
Previous works have evaluated memorization by comparing model outputs with training corpora, examining how factors such as data duplication, model size, and prompt length influence memorization. However, analyzing these extensive training corpora is
Externí odkaz:
http://arxiv.org/abs/2403.00510