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AcademicGPT: Empowering Academic Research

Authors :
Wei, Shufa
Xu, Xiaolong
Qi, Xianbiao
Yin, Xi
Xia, Jun
Ren, Jingyi
Tang, Peijun
Zhong, Yuxiang
Chen, Yihao
Ren, Xiaoqin
Liang, Yuxin
Huang, Liankai
Xie, Kai
Gui, Weikang
Tan, Wei
Sun, Shuanglong
Hu, Yongquan
Liu, Qinxian
Li, Nanjin
Dai, Chihao
Wang, Lihua
Liu, Xiaohui
Zhang, Lei
Xie, Yutao
Publication Year :
2023

Abstract

Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks. Yet, many of these advanced LLMs are tailored for broad, general-purpose applications. In this technical report, we introduce AcademicGPT, designed specifically to empower academic research. AcademicGPT is a continual training model derived from LLaMA2-70B. Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others. While it may not be extensive in data scale, AcademicGPT marks our initial venture into a domain-specific GPT tailored for research area. We evaluate AcademicGPT on several established public benchmarks such as MMLU and CEval, as well as on some specialized academic benchmarks like PubMedQA, SCIEval, and our newly-created ComputerScienceQA, to demonstrate its ability from general knowledge ability, to Chinese ability, and to academic ability. Building upon AcademicGPT's foundation model, we also developed several applications catered to the academic area, including General Academic Question Answering, AI-assisted Paper Reading, Paper Review, and AI-assisted Title and Abstract Generation.<br />Comment: Technical Report. arXiv admin note: text overlap with arXiv:2310.12081, arXiv:2310.10053 by other authors

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2311.12315
Document Type :
Working Paper