Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Dou, Chengfeng"'
Autor:
Dou, Chengfeng, Zhang, Ying, Jin, Zhi, Jiao, Wenpin, Zhao, Haiyan, Zhao, Yongqiang, Tao, Zhengwei
This research examines the use of Reinforcement Learning from AI Feedback (RLAIF) techniques to improve healthcare dialogue models, with the aim of tackling the challenges of preference-aligned data annotation while reducing the reliance on medical e
Externí odkaz:
http://arxiv.org/abs/2410.04112
The use of large language models in medical dialogue generation has garnered significant attention, with a focus on improving response quality and fluency. While previous studies have made progress in optimizing model performance for single-round med
Externí odkaz:
http://arxiv.org/abs/2401.05695
Autor:
Zhao, Yongqiang, Li, Zhenyu, Jin, Zhi, Zhang, Feng, Zhao, Haiyan, Dou, Chengfeng, Tao, Zhengwei, Xu, Xinhai, Liu, Donghong
The Multi-Modal Large Language Model (MLLM) refers to an extension of the Large Language Model (LLM) equipped with the capability to receive and infer multi-modal data. Spatial awareness stands as one of the crucial abilities of MLLM, encompassing di
Externí odkaz:
http://arxiv.org/abs/2310.20357
The patient-centered medical dialogue systems strive to offer diagnostic interpretation services to users who are less knowledgeable about medical knowledge, through emphasizing the importance of providing responses specific to the patients. It is di
Externí odkaz:
http://arxiv.org/abs/2305.11508
Publikováno v:
In Journal of Systems Architecture December 2020 111
Medical dialogue systems aim to provide accurate answers to patients, necessitating specific domain knowledge. Recent advancements in Large Language Models (LLMs) have demonstrated their exceptional capabilities in the medical Q&A domain, indicating
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83e020325a387ad874bfc39d3525153c
http://arxiv.org/abs/2305.11508
http://arxiv.org/abs/2305.11508