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pro vyhledávání: '"Li, Baohang"'
Autor:
Fu, Chengpeng, Feng, Xiaocheng, Huang, Yichong, Huo, Wenshuai, Li, Baohang, Wang, Hui, Qin, Bin, Liu, Ting
Leveraging large language models for machine translation has demonstrated promising results. However, it does require the large language models to possess the capability of handling both the source and target languages in machine translation. When it
Externí odkaz:
http://arxiv.org/abs/2405.02933
Large language models (LLMs) exhibit complementary strengths in various tasks, motivating the research of LLM ensembling. However, existing work focuses on training an extra reward model or fusion model to select or combine all candidate answers, pos
Externí odkaz:
http://arxiv.org/abs/2404.12715
Autor:
Huang, Yichong, Li, Baohang, Feng, Xiaocheng, Fu, Chengpeng, Huo, Wenshuai, Liu, Ting, Qin, Bing
Large Language models (LLMs) have exhibited remarkable abilities in understanding complex texts, offering a promising path towards human-like translation performance. However, this study reveals the misalignment between the translation-specific under
Externí odkaz:
http://arxiv.org/abs/2401.05072
Multilingual neural machine translation has witnessed remarkable progress in recent years. However, the long-tailed distribution of multilingual corpora poses a challenge of Pareto optimization, i.e., optimizing for some languages may come at the cos
Externí odkaz:
http://arxiv.org/abs/2305.15718
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