Zobrazeno 1 - 10
of 699
pro vyhledávání: '"Wu Yuxiang"'
Autor:
Zhou, Xuyang, Bienvenu, Baptiste, Wu, Yuxiang, da Silva, Alisson Kwiatkowski, Ophus, Colin, Raabe, Dierk
Synthesizing distinct phases and controlling the crystalline defects in them are key concepts in materials and process design. These approaches are usually described by decoupled theories, with the former resting on equilibrium thermodynamics and the
Externí odkaz:
http://arxiv.org/abs/2408.09213
Publikováno v:
Results in Physics, Vol 15, Iss , Pp - (2019)
In phase measuring deflectometry (PMD), the existence of parasitic reflections at the rear surface of transparent objects will lead to ‘ghosted’ fringe patterns, which results in phase error. Accurately extracting the phase from the 'ghosted' fri
Externí odkaz:
https://doaj.org/article/49375212e06e43d1b80a0688893e996a
Autor:
Zhao, Yu, Qu, Yuanbin, Staniszewski, Konrad, Tworkowski, Szymon, Liu, Wei, Miłoś, Piotr, Wu, Yuxiang, Minervini, Pasquale
Most language model pre-training frameworks concatenate multiple documents into fixed-length sequences and use causal masking to compute the likelihood of each token given its context; this strategy is widely adopted due to its simplicity and efficie
Externí odkaz:
http://arxiv.org/abs/2402.13991
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have explored human-like problem-solving strategies, such as self-correct, to
Externí odkaz:
http://arxiv.org/abs/2311.08152
Recent studies demonstrated that large language models (LLMs) can excel in many tasks via in-context learning (ICL). However, recent works show that ICL-prompted models tend to produce inaccurate results when presented with adversarial inputs. In thi
Externí odkaz:
http://arxiv.org/abs/2311.07556
Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time consuming and costly. However, DST extends beyond simple slot-filling and requires effective updating strategies fo
Externí odkaz:
http://arxiv.org/abs/2310.10520
Autor:
Zhu, Zhengyan, Wu, Yuxiang, Fan, Shengtai, Fan, Yiliang, Li, Yiwen, Ye, Yongze, Zhu, Xiyu, Zhang, Haijun, Wen, Hai-Hu
The kagome lattice is very attractive as it can host many novel quantum states, such as the charge density wave, superconductivity, quantum spin liquid, etc. Meanwhile, iridates often exhibit a strong spin-orbit coupling (SOC) effect due to the large
Externí odkaz:
http://arxiv.org/abs/2310.03609
The burgeoning progress in the field of Large Language Models (LLMs) heralds significant benefits due to their unparalleled capacities. However, it is critical to acknowledge the potential misuse of these models, which could give rise to a spectrum o
Externí odkaz:
http://arxiv.org/abs/2305.12680
Autor:
Dong, Guanting, Wang, Zechen, Wang, Liwen, Guo, Daichi, Fu, Dayuan, Wu, Yuxiang, Zeng, Chen, Li, Xuefeng, Hui, Tingfeng, He, Keqing, Cui, Xinyue, Gao, Qixiang, Xu, Weiran
Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing prototype-based sequence labeling models tend to memorize entity mentions which would be easily confused by close prototypes
Externí odkaz:
http://arxiv.org/abs/2302.13610
Autor:
Guo, Daichi, Dong, Guanting, Fu, Dayuan, Wu, Yuxiang, Zeng, Chen, Hui, Tingfeng, Wang, Liwen, Li, Xuefeng, Wang, Zechen, He, Keqing, Cui, Xinyue, Xu, Weiran
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems. To address this issue, we propose an OOV robust slot filling mod
Externí odkaz:
http://arxiv.org/abs/2302.13584