Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Yang, Muyun"'
Autor:
Chen, Andong, Lou, Lianzhang, Chen, Kehai, Bai, Xuefeng, Xiang, Yang, Yang, Muyun, Zhao, Tiejun, Zhang, Min
Large language models (LLMs) have shown remarkable performance in general translation tasks. However, the increasing demand for high-quality translations that are not only adequate but also fluent and elegant. To assess the extent to which current LL
Externí odkaz:
http://arxiv.org/abs/2408.09945
Autor:
Jiang, Ruili, Chen, Kehai, Bai, Xuefeng, He, Zhixuan, Li, Juntao, Yang, Muyun, Zhao, Tiejun, Nie, Liqiang, Zhang, Min
The recent surge of versatile large language models (LLMs) largely depends on aligning increasingly capable foundation models with human intentions by preference learning, enhancing LLMs with excellent applicability and effectiveness in a wide range
Externí odkaz:
http://arxiv.org/abs/2406.11191
Autor:
Chen, Andong, Lou, Lianzhang, Chen, Kehai, Bai, Xuefeng, Xiang, Yang, Yang, Muyun, Zhao, Tiejun, Zhang, Min
Recently, large language models (LLMs) enhanced by self-reflection have achieved promising performance on machine translation. The key idea is guiding LLMs to generate translation with human-like feedback. However, existing self-reflection methods la
Externí odkaz:
http://arxiv.org/abs/2406.07232
The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect - model
Externí odkaz:
http://arxiv.org/abs/2403.04222
Recently, there has been a growing trend of utilizing Large Language Model (LLM) to evaluate the quality of other LLMs. Many studies have employed proprietary close-source models, especially GPT-4, as the evaluator. Alternatively, other works have fi
Externí odkaz:
http://arxiv.org/abs/2403.02839
Low-Rank Adaptation (LoRA) is currently the most commonly used Parameter-efficient fine-tuning (PEFT) method, it introduces auxiliary parameters for each layer to fine-tune the pre-trained model under limited computing resources. However, it still fa
Externí odkaz:
http://arxiv.org/abs/2402.07721
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 4, p e17622 (2020)
BackgroundDeidentification of clinical records is a critical step before their publication. This is usually treated as a type of sequence labeling task, and ensemble learning is one of the best performing solutions. Under the framework of multi-learn
Externí odkaz:
https://doaj.org/article/41351ce694b645f9b1b581443c03b693
Autor:
Liang, Xinnian, Zhou, Zefan, Huang, Hui, Wu, Shuangzhi, Xiao, Tong, Yang, Muyun, Li, Zhoujun, Bian, Chao
Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and completely ignore word information. Although Whole Word Masking can alleviate
Externí odkaz:
http://arxiv.org/abs/2303.10893
Publikováno v:
In Information Fusion October 2024 110