title: Shai: A large language model for asset management
summary: 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.
paper_authors._id: 658a4576dda02636b017c586
paper_authors.name: Zhongyang Guo
paper_authors._id: 658a4576dda02636b017c587
paper_authors.name: Guanran Jiang
paper_authors._id: 658a4576dda02636b017c588
paper_authors.name: Zhongdan Zhang
paper_authors._id: 658a4576dda02636b017c589
paper_authors.name: Peng Li
paper_authors._id: 658a4576dda02636b017c58a
paper_authors.name: Zhefeng Wang
paper_authors._id: 658a4576dda02636b017c58b
paper_authors.name: Yinchun Wang
paper_title: Shai: A large language model for asset management
paper_summary: 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.
paper_ai_keywords: large language model continuous pre-training fine-tuning targeted corpus caption models evaluation framework professional qualification exams tailored tasks open-ended question answering safety assessments automated evaluation human judgment 10B-level large language models