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