Zobrazeno 1 - 10
of 831
pro vyhledávání: '"Li, RuiRui"'
Autor:
Cheng, Kewei, Yang, Jingfeng, Jiang, Haoming, Wang, Zhengyang, Huang, Binxuan, Li, Ruirui, Li, Shiyang, Li, Zheng, Gao, Yifan, Li, Xian, Yin, Bing, Sun, Yizhou
Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), most studies have failed to rigorously differentiate between inductive a
Externí odkaz:
http://arxiv.org/abs/2408.00114
Pre-trained language models, trained on large-scale corpora, demonstrate strong generalizability across various NLP tasks. Fine-tuning these models for specific tasks typically involves updating all parameters, which is resource-intensive. Parameter-
Externí odkaz:
http://arxiv.org/abs/2406.10777
Human pose estimation is a fundamental and challenging task in computer vision. Larger-scale and more accurate keypoint annotations, while helpful for improving the accuracy of supervised pose estimation, are often expensive and difficult to obtain.
Externí odkaz:
http://arxiv.org/abs/2404.14835
Autor:
Jin, Bowen, Xie, Chulin, Zhang, Jiawei, Roy, Kashob Kumar, Zhang, Yu, Li, Zheng, Li, Ruirui, Tang, Xianfeng, Wang, Suhang, Meng, Yu, Han, Jiawei
Publikováno v:
ACL 2024
Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora t
Externí odkaz:
http://arxiv.org/abs/2404.07103
Label noise is a common issue in real-world datasets that inevitably impacts the generalization of models. This study focuses on robust classification tasks where the label noise is instance-dependent. Estimating the transition matrix accurately in t
Externí odkaz:
http://arxiv.org/abs/2404.04800
Autor:
Chen, Xiusi, Wen, Hongzhi, Nag, Sreyashi, Luo, Chen, Yin, Qingyu, Li, Ruirui, Li, Zheng, Wang, Wei
With the rapid development of large language models (LLMs), aligning LLMs with human values and societal norms to ensure their reliability and safety has become crucial. Reinforcement learning with human feedback (RLHF) and Constitutional AI (CAI) ha
Externí odkaz:
http://arxiv.org/abs/2403.18341
Autor:
Wei, Tianxin, Jin, Bowen, Li, Ruirui, Zeng, Hansi, Wang, Zhengyang, Sun, Jianhui, Yin, Qingyu, Lu, Hanqing, Wang, Suhang, He, Jingrui, Tang, Xianfeng
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Our daily choices, especially in domains like fashion and retail, are su
Externí odkaz:
http://arxiv.org/abs/2403.10667
Autor:
Wang, Haoyu, Li, Ruirui, Jiang, Haoming, Tian, Jinjin, Wang, Zhengyang, Luo, Chen, Tang, Xianfeng, Cheng, Monica, Zhao, Tuo, Gao, Jing
Retrieval-augmented Large Language Models (LLMs) offer substantial benefits in enhancing performance across knowledge-intensive scenarios. However, these methods often face challenges with complex inputs and encounter difficulties due to noisy knowle
Externí odkaz:
http://arxiv.org/abs/2402.11129
Autor:
Xu, Yinglun, Suresh, Tarun, Gumaste, Rohan, Zhu, David, Li, Ruirui, Wang, Zhengyang, Jiang, Haoming, Tang, Xianfeng, Yin, Qingyu, Cheng, Monica Xiao, Zeng, Qi, Zhang, Chao, Singh, Gagandeep
Preference-based reinforcement learning (PBRL) in the offline setting has succeeded greatly in industrial applications such as chatbots. A two-step learning framework where one applies a reinforcement learning step after a reward modeling step has be
Externí odkaz:
http://arxiv.org/abs/2401.00330
Autor:
Jin, Bowen, Zeng, Hansi, Wang, Guoyin, Chen, Xiusi, Wei, Tianxin, Li, Ruirui, Wang, Zhengyang, Li, Zheng, Li, Yang, Lu, Hanqing, Wang, Suhang, Han, Jiawei, Tang, Xianfeng
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by firs
Externí odkaz:
http://arxiv.org/abs/2310.07815