Zobrazeno 1 - 10
of 291
pro vyhledávání: '"Li, Siheng"'
Autor:
Yang, Cheng, Huang, Guoping, Yu, Mo, Zhang, Zhirui, Li, Siheng, Yang, Mingming, Shi, Shuming, Yang, Yujiu, Liu, Lemao
Word-level AutoCompletion(WLAC) is a rewarding yet challenging task in Computer-aided Translation. Existing work addresses this task through a classification model based on a neural network that maps the hidden vector of the input context into its co
Externí odkaz:
http://arxiv.org/abs/2407.20083
Autor:
Cai, Deng, Li, Huayang, Fu, Tingchen, Li, Siheng, Xu, Weiwen, Li, Shuaiyi, Cao, Bowen, Zhang, Zhisong, Huang, Xinting, Cui, Leyang, Wang, Yan, Liu, Lemao, Watanabe, Taro, Shi, Shuming
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications. In this paper, we demonstrate the interchangeability of three popular and distinct adaptation too
Externí odkaz:
http://arxiv.org/abs/2406.16377
Autor:
Shi, Chufan, Yang, Cheng, Liu, Yaxin, Shui, Bo, Wang, Junjie, Jing, Mohan, Xu, Linran, Zhu, Xinyu, Li, Siheng, Zhang, Yuxiang, Liu, Gongye, Nie, Xiaomei, Cai, Deng, Yang, Yujiu
We introduce a new benchmark, ChartMimic, aimed at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-intensive visual charts and textual instructions as inputs, requiring L
Externí odkaz:
http://arxiv.org/abs/2406.09961
Autor:
Shi, Chufan, Yang, Cheng, Zhu, Xinyu, Wang, Jiahao, Wu, Taiqiang, Li, Siheng, Cai, Deng, Yang, Yujiu, Meng, Yu
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activates a different subset of experts determined by a routing mechanism. Ho
Externí odkaz:
http://arxiv.org/abs/2405.14507
Transition metal dichalcogenides (TMDs) are a class of two-dimensional (2D) materials been widely studied for emerging electronic properties. In this work, we use computational simulations to examine the water adsorption on TMDs systematically and th
Externí odkaz:
http://arxiv.org/abs/2402.11470
Autor:
Li, Huayang, Li, Siheng, Cai, Deng, Wang, Longyue, Liu, Lemao, Watanabe, Taro, Yang, Yujiu, Shi, Shuming
Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle various real-world tasks through their natural language interfaces. However,
Externí odkaz:
http://arxiv.org/abs/2309.08637
Autor:
Li, Siheng, Yang, Cheng, Yin, Yichun, Zhu, Xinyu, Cheng, Zesen, Shang, Lifeng, Jiang, Xin, Liu, Qun, Yang, Yujiu
Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stymied by the scarcity of training data. To alleviate this problem, we pr
Externí odkaz:
http://arxiv.org/abs/2308.06507
Autor:
Li, Siheng, Yin, Yichun, Yang, Cheng, Jiang, Wangjie, Li, Yiwei, Cheng, Zesen, Shang, Lifeng, Jiang, Xin, Liu, Qun, Yang, Yujiu
Hot news is one of the most popular topics in daily conversations. However, news grounded conversation has long been stymied by the lack of well-designed task definition and scarce data. In this paper, we propose a novel task, Proactive News Grounded
Externí odkaz:
http://arxiv.org/abs/2308.06501
Autor:
Cheng, Zesen, Jin, Peng, Li, Hao, Li, Kehan, Li, Siheng, Ji, Xiangyang, Liu, Chang, Chen, Jie
The top-down and bottom-up methods are two mainstreams of referring segmentation, while both methods have their own intrinsic weaknesses. Top-down methods are chiefly disturbed by Polar Negative (PN) errors owing to the lack of fine-grained cross-mod
Externí odkaz:
http://arxiv.org/abs/2306.10750
Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world. However, due to the dynamic and ever-changing nature of real-world facts, the answer can be completely different when the ti
Externí odkaz:
http://arxiv.org/abs/2305.14221