Zobrazeno 1 - 10
of 530
pro vyhledávání: '"LIU Boyi"'
Publikováno v:
Guan'gai paishui xuebao, Vol 41, Iss 10, Pp 65-72 (2022)
【Objective】 Nitrogen fertilizer is essential to maintaining crop growth and yield, but its excessive or insufficient application has been a problem facing rice production in China. The purpose of this paper is to investigate the effect of soluble
Externí odkaz:
https://doaj.org/article/5c3dfb9de45d4c15beceead5f3929a58
Competition-level code generation tasks pose significant challenges for current state-of-the-art large language models (LLMs). For example, on the LiveCodeBench-Hard dataset, models such as O1-Mini and O1-Preview achieve pass@1 rates of only 0.366 an
Externí odkaz:
http://arxiv.org/abs/2412.12544
Autor:
Bytedance-Seed-Foundation-Code-Team, Cheng, Yao, Chen, Jianfeng, Chen, Jie, Chen, Li, Chen, Liyu, Chen, Wentao, Chen, Zhengyu, Geng, Shijie, Li, Aoyan, Li, Bo, Li, Bowen, Li, Linyi, Liu, Boyi, Liu, Jerry, Liu, Kaibo, Liu, Qi, Liu, Shukai, Liu, Siyao, Liu, Tianyi, Liu, Tingkai, Liu, Yongfei, Long, Rui, Mai, Jing, Ning, Guanghan, Peng, Z. Y., Shen, Kai, Su, Jiahao, Su, Jing, Sun, Tao, Sun, Yifan, Tao, Yunzhe, Wang, Guoyin, Wang, Siwei, Wang, Xuwu, Wang, Yite, Wang, Zihan, Xia, Jinxiang, Xiang, Liang, Xiao, Xia, Xiao, Yongsheng, Xi, Chenguang, Xin, Shulin, Xu, Jingjing, Xu, Shikun, Yang, Hongxia, Yang, Jack, Yang, Yingxiang, Yuan, Jianbo, Zhang, Jun, Zhang, Yufeng, Zhang, Yuyu, Zheng, Shen, Zhu, He, Zhu, Ming
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most existing datasets only evaluate limited application domains. To address thi
Externí odkaz:
http://arxiv.org/abs/2412.00535
Direct preference learning offers a promising and computation-efficient beyond supervised fine-tuning (SFT) for improving code generation in coding large language models (LMs). However, the scarcity of reliable preference data is a bottleneck for the
Externí odkaz:
http://arxiv.org/abs/2411.13611
The rapid adoption of large language models (LLMs) presents new challenges for existing network architectures due to significant peak traffic and high communication uncertainty. Traditional wireless networks struggle to support efficiently, leading t
Externí odkaz:
http://arxiv.org/abs/2410.18499
Autor:
Wang, Xuwu, Cui, Qiwen, Tao, Yunzhe, Wang, Yiran, Chai, Ziwei, Han, Xiaotian, Liu, Boyi, Yuan, Jianbo, Su, Jing, Wang, Guoyin, Liu, Tingkai, Chen, Liyu, Liu, Tianyi, Sun, Tao, Zhang, Yufeng, Zheng, Sirui, You, Quanzeng, Yang, Yang, Yang, Hongxia
Large language models (LLMs) have become increasingly pivotal across various domains, especially in handling complex data types. This includes structured data processing, as exemplified by ChartQA and ChatGPT-Ada, and multimodal unstructured data pro
Externí odkaz:
http://arxiv.org/abs/2410.00773
Autor:
Zhan, Yi, Sun, Yang, Weng, Han, Cui, Longjie, Wang, Guifeng, Xie, Jiajun, Tian, Yu, Yin, Xiaoming, Liu, Boyi, Huang, Dongchi
In this paper, we propose a novel graph-based methodology to evaluate the functional correctness of SQL generation. Conventional metrics for assessing SQL code generation, such as matching-based and execution-based methods (e.g., exact set match and
Externí odkaz:
http://arxiv.org/abs/2407.14530
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
Autor:
Liu, Zhihan, Lu, Miao, Zhang, Shenao, Liu, Boyi, Guo, Hongyi, Yang, Yingxiang, Blanchet, Jose, Wang, Zhaoran
Aligning generative models with human preference via RLHF typically suffers from overoptimization, where an imperfectly learned reward model can misguide the generative model to output undesired responses. We investigate this problem in a principled
Externí odkaz:
http://arxiv.org/abs/2405.16436
This paper presents EdgeLoc, an infrastructure-assisted, real-time localization system for autonomous driving that addresses the incompatibility between traditional localization methods and deep learning approaches. The system is built on top of the
Externí odkaz:
http://arxiv.org/abs/2405.12120
Autor:
Zhang, Yufeng, Chen, Liyu, Liu, Boyi, Yang, Yingxiang, Cui, Qiwen, Tao, Yunzhe, Yang, Hongxia
Recent advances in reinforcement learning (RL) algorithms aim to enhance the performance of language models at scale. Yet, there is a noticeable absence of a cost-effective and standardized testbed tailored to evaluating and comparing these algorithm
Externí odkaz:
http://arxiv.org/abs/2403.07191