人气
计算机科学
领域(数学分析)
语言模型
比例(比率)
稀缺
领域(数学)
遗忘
可扩展性
建筑
人工智能
数据科学
语言学
操作系统
心理学
数学分析
社会心理学
哲学
物理
艺术
数学
量子力学
纯数学
经济
视觉艺术
微观经济学
标识
DOI:10.1145/3583780.3615285
摘要
Recently, with the popularity of ChatGPT, large-scale language models have experienced rapid development. However, there is a scarcity of open-sourced chat models specifically designed for the Chinese language, especially in the field of Chinese finance, at the scale of hundreds of billions. To address this gap, we introduce XuanYuan 2.0, the largest Chinese chat model to date, built upon the BLOOM-176B architecture. Additionally, we propose a novel training method called hybrid-tuning to mitigate catastrophic forgetting. By integrating general and domain-specific knowledge, as well as combining the stages of pre-training and fine-tuning, XuanYuan 2.0 is capable of providing accurate and contextually appropriate responses in the Chinese financial domain.
科研通智能强力驱动
Strongly Powered by AbleSci AI