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.
@article{arxiv.2311.12315,
title = {AcademicGPT: Empowering Academic Research},
author = {Shufa Wei and Xiaolong Xu and Xianbiao Qi and Xi Yin and Jun Xia and Jingyi Ren and Peijun Tang and Yuxiang Zhong and Yihao Chen and Xiaoqin Ren and Yuxin Liang and Liankai Huang and Kai Xie and Weikang Gui and Wei Tan and Shuanglong Sun and Yongquan Hu and Qinxian Liu and Nanjin Li and Chihao Dai and Lihua Wang and Xiaohui Liu and Lei Zhang and Yutao Xie},
journal= {arXiv preprint arXiv:2311.12315},
year = {2023}
}
Comments
Technical Report. arXiv admin note: text overlap with arXiv:2310.12081, arXiv:2310.10053 by other authors