Výsledky vyhledávání

Report

Fully Open Source Moxin-7B Technical Report

Autor: Zhao, Pu, Shen, Xuan, Kong, Zhenglun, Shen, Yixin, Chang, Sung-En, Rupprecht, Timothy, Lu, Lei, Nan, Enfu, Yang, Changdi, He, Yumei, Xu, Xingchen, Huang, Yu, Wang, Wei, Chen, Yue, He, Yong, Wang, Yanzhi

Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread at

Externí odkaz: http://arxiv.org/abs/2412.06845

Zobrazit plný text záznamu

Report

Learning to Adopt Generative AI

Autor: Ma, Lijia, Xu, Xingchen, He, Yumei, Tan, Yong

Recent advancements in generative AI, exemplified by ChatGPT, have dramatically transformed how people access information. Despite its powerful capabilities, the benefits it provides may not be equally distributed among individuals - a phenomenon ref

Externí odkaz: http://arxiv.org/abs/2410.19806

Zobrazit plný text záznamu

Report

Digital Avatars: Framework Development and Their Evaluation

Autor: Rupprecht, Timothy, Chang, Sung-En, Wu, Yushu, Lu, Lei, Nan, Enfu, Li, Chih-hsiang, Lai, Caiyue, Li, Zhimin, Hu, Zhijun, He, Yumei, Kaeli, David, Wang, Yanzhi

Publikováno v: 2024 Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track. Pages 8780-8783

We present a novel prompting strategy for artificial intelligence driven digital avatars. To better quantify how our prompting strategy affects anthropomorphic features like humor, authenticity, and favorability we present Crowd Vote - an adaptation

Externí odkaz: http://arxiv.org/abs/2408.04068

Zobrazit plný text záznamu