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pro vyhledávání: '"Lei, Yikun"'
Autor:
Zhong, Meizhi, Liu, Xikai, Zhang, Chen, Lei, Yikun, Gao, Yan, Hu, Yao, Chen, Kehai, Zhang, Min
Large Language models (LLMs) have become a research hotspot. To accelerate the inference of LLMs, storing computed caches in memory has become the standard technique. However, as the inference length increases, growing KV caches might lead to out-of-
Externí odkaz:
http://arxiv.org/abs/2412.09036
Autor:
Sun, Haoran, Jin, Renren, Xu, Shaoyang, Pan, Leiyu, Supryadi, Cui, Menglong, Du, Jiangcun, Lei, Yikun, Yang, Lei, Shi, Ling, Xiao, Juesi, Zhu, Shaolin, Xiong, Deyi
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource languages. To mitigate this challenge, we present FuxiTranyu, an open-sou
Externí odkaz:
http://arxiv.org/abs/2408.06273
Autor:
Zhong, Meizhi, Zhang, Chen, Lei, Yikun, Liu, Xikai, Gao, Yan, Hu, Yao, Chen, Kehai, Zhang, Min
Enabling LLMs to handle lengthy context is currently a research hotspot. Most LLMs are built upon rotary position embedding (RoPE), a popular position encoding method. Therefore, a prominent path is to extrapolate the RoPE trained on comparably short
Externí odkaz:
http://arxiv.org/abs/2406.13282
In this paper, we employ Singular Value Canonical Correlation Analysis (SVCCA) to analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages. SVCCA enables us to estimate representational similarity
Externí odkaz:
http://arxiv.org/abs/2310.20456
Publikováno v:
In Expert Systems With Applications 1 August 2024 247
SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension. Most existing approaches propose to concat question and option together to form a context-aware model. However, we argue that s
Externí odkaz:
http://arxiv.org/abs/2105.12051
Akademický článek
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Publikováno v:
IOP Conference Series: Materials Science & Engineering; Feb2020, Vol. 751 Issue 1, p1-1, 1p