Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Liu, Hanmeng"'
Recent advancements in Chain-of-Thought (CoT) reasoning utilize complex modules but are hampered by high token consumption, limited applicability, and challenges in reproducibility. This paper conducts a critical evaluation of CoT prompting, extendin
Externí odkaz:
http://arxiv.org/abs/2406.06580
Chain-of-Thought (CoT) prompting has emerged as a pivotal technique for augmenting the inferential capabilities of language models during reasoning tasks. Despite its advancements, CoT often grapples with challenges in validating reasoning validity a
Externí odkaz:
http://arxiv.org/abs/2404.18130
Recently, large language models (LLMs), including notable models such as GPT-4 and burgeoning community models, have showcased significant general language understanding abilities. However, there has been a scarcity of attempts to assess the logical
Externí odkaz:
http://arxiv.org/abs/2310.09107
Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the
Externí odkaz:
http://arxiv.org/abs/2305.12147
Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performanc
Externí odkaz:
http://arxiv.org/abs/2304.03439
Autor:
Yang, Linyi, Zhang, Shuibai, Qin, Libo, Li, Yafu, Wang, Yidong, Liu, Hanmeng, Wang, Jindong, Xie, Xing, Zhang, Yue
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase. However, the out-of-distribution (OOD) generalization p
Externí odkaz:
http://arxiv.org/abs/2211.08073
Publikováno v:
In Journal of Analytical and Applied Pyrolysis September 2024 182
Autor:
Yu, Ting, Wang, Zhao, Li, HaiTao, Zheng, Kai, Luo, JunTao, Liu, HanMeng, Li, YanYun, Ai, LiPing, Wang, JiaLin, Song, YuJie, Ye, Zheng, Zhang, YueXing, Wang, HuiJuan, Chen, XueLi, Chen, YongLin
Publikováno v:
In Journal of Water Process Engineering December 2024 68
Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-
Externí odkaz:
http://arxiv.org/abs/2110.07310
Autor:
Wu, Jing, Hu, Guangxing, Zhao, Juanjuan, Zou, Changxiu, Xing, Huanhuan, Shen, Wei, Li, Zhuang, Liu, Hanmeng
Publikováno v:
In Applied Surface Science 1 August 2024 663