Shai: A large language model for asset management
This paper introduces "Shai" a 10B level large language model specifically
designed for the asset management industry, built upon an open-source
foundational model. With continuous pre-training and fine-tuning using a
targeted corpus, Shai demonstrates enhanced performance in tasks relevant to
its domain, outperforming baseline models. Our research includes the
development of an innovative evaluation framework, which integrates
professional qualification exams, tailored tasks, open-ended question
answering, and safety assessments, to comprehensively assess Shai's
capabilities. Furthermore, we discuss the challenges and implications of
utilizing large language models like GPT-4 for performance assessment in asset
management, suggesting a combination of automated evaluation and human
judgment. Shai's development, showcasing the potential and versatility of
10B-level large language models in the financial sector with significant
performance and modest computational requirements, hopes to provide practical
insights and methodologies to assist industry peers in their similar endeavors.
658a4576dda02636b017c586
Zhongyang Guo
658a4576dda02636b017c587
Guanran Jiang
658a4576dda02636b017c588
Zhongdan Zhang
658a4576dda02636b017c589
Peng Li
658a4576dda02636b017c58a
Zhefeng Wang
658a4576dda02636b017c58b
Yinchun Wang
Shai: A large language model for asset management
This paper introduces "Shai" a 10B level large language model specifically
designed for the asset management industry, built upon an open-source
foundational model. With continuous pre-training and fine-tuning using a
targeted corpus, Shai demonstrates enhanced performance in tasks relevant to
its domain, outperforming baseline models. Our research includes the
development of an innovative evaluation framework, which integrates
professional qualification exams, tailored tasks, open-ended question
answering, and safety assessments, to comprehensively assess Shai's
capabilities. Furthermore, we discuss the challenges and implications of
utilizing large language models like GPT-4 for performance assessment in asset
management, suggesting a combination of automated evaluation and human
judgment. Shai's development, showcasing the potential and versatility of
10B-level large language models in the financial sector with significant
performance and modest computational requirements, hopes to provide practical
insights and methodologies to assist industry peers in their similar endeavors.
Shai, a 10B-level large language model for asset management, demonstrates enhanced performance through continuous pre-training and fine-tuning, using a comprehensive evaluation framework that includes professional qualification exams and safety assessments.
large language model continuous pre-training fine-tuning targeted corpus evaluation framework professional qualification exams tailored tasks open-ended question answering safety assessments automated evaluation human judgment