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LLMs for Doctors: Leveraging Medical LLMs to Assist Doctors, Not Replace Them

Autor: Xie, Wenya, Xiao, Qingying, Zheng, Yu, Wang, Xidong, Chen, Junying, Ji, Ke, Gao, Anningzhe, Wan, Xiang, Jiang, Feng, Wang, Benyou

The recent success of Large Language Models (LLMs) has had a significant impact on the healthcare field, providing patients with medical advice, diagnostic information, and more. However, due to a lack of professional medical knowledge, patients are

Externí odkaz: http://arxiv.org/abs/2406.18034

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LLMs Could Autonomously Learn Without External Supervision

Autor: Ji, Ke, Chen, Junying, Gao, Anningzhe, Xie, Wenya, Wan, Xiang, Wang, Benyou

In the quest for super-human performance, Large Language Models (LLMs) have traditionally been tethered to human-annotated datasets and predefined training objectives-a process that is both labor-intensive and inherently limited. This paper presents

Externí odkaz: http://arxiv.org/abs/2406.00606

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MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria

Autor: Ge, Wentao, Chen, Shunian, Chen, Guiming Hardy, Chen, Junying, Chen, Zhihong, Chen, Nuo, Xie, Wenya, Yan, Shuo, Zhu, Chenghao, Lin, Ziyue, Dingjie, Song, Wang, Xidong, Gao, Anningzhe, Zhiyi, Zhang, Li, Jianquan, Wan, Xiang, Wang, Benyou

Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating queries without considering user experiences, inadequately addressing the nua

Externí odkaz: http://arxiv.org/abs/2311.13951

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HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs

Autor: Chen, Junying, Wang, Xidong, Ji, Ke, Gao, Anningzhe, Jiang, Feng, Chen, Shunian, Zhang, Hongbo, Song, Dingjie, Xie, Wenya, Kong, Chuyi, Li, Jianquan, Wan, Xiang, Li, Haizhou, Wang, Benyou

Adapting a language model into a specific domain, a.k.a `domain adaption', is a common practice when specialized knowledge, e.g. medicine, is not encapsulated in a general language model like Llama2. The challenge lies in the heterogeneity of data ac

Externí odkaz: http://arxiv.org/abs/2311.09774

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Coarse-to-fine Hybrid 3D Mapping System with Co-calibrated Omnidirectional Camera and Non-repetitive LiDAR

Autor: Miao, Ziliang, He, Buwei, Xie, Wenya, Zhao, Wenquan, Huang, Xiao, Bai, Jian, Hong, Xiaoping

This paper presents a novel 3D mapping robot with an omnidirectional field-of-view (FoV) sensor suite composed of a non-repetitive LiDAR and an omnidirectional camera. Thanks to the non-repetitive scanning nature of the LiDAR, an automatic targetless

Externí odkaz: http://arxiv.org/abs/2301.12934

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Akademický článek

Knockout of transcription factor OsERF65 enhances ROS scavenging ability and confers resistance to rice sheath blight.

Autor: Xie, Wenya^1,2 (AUTHOR), Cao, Wenlei³ (AUTHOR), Lu, Shuaibing¹ (AUTHOR), Zhao, Jianhua¹ (AUTHOR), Shi, Xiaopin¹ (AUTHOR), Yue, Xuanyu¹ (AUTHOR), Wang, Guangda¹ (AUTHOR), Feng, Zhiming^1,2 (AUTHOR), Hu, Keming^1,2 (AUTHOR), Chen, Zongxiang^1,2 (AUTHOR), Zuo, Shimin^1,2,4 (AUTHOR) smzuo@yzu.edu.cn

Publikováno v: Molecular Plant Pathology. Dec2023, Vol. 24 Issue 12, p1535-1551. 17p.