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 10Blevel 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. Recent advancements in Large Language Models (LLMs) have resulted in breakthroughs, with 100B-level models like GPT-4 [1], LLaMa2 [2], ChatGLM[3], BLOOM[4], Falcon[5] and PaLM2[6] leading the way in natural language processing (NLP) capabilities. These models have shown an exceptional ability to generate natural and coherent text, understand complex contexts, and adapt to a wide variety of tasks and scenarios. Besides the general LLM development, domain specific LLM development is also flourishing, where the domains span from law[7; 8; 9] to health care[10; 11; 12; 13] and finance[14; 15; 16; 17] etc.