  Large language models (LLMs) are increasingly being applied across various
specialized fields, leveraging their extensive knowledge to empower a multitude
of scenarios within these domains. However, each field encompasses a variety of
specific tasks that require learning, and the diverse, heterogeneous data
across these domains can lead to conflicts during model task transfer. In
response to this challenge, our study introduces an Adaptive Semantic Space
Learning (ASSL) framework, which utilizes the adaptive reorganization of data
distributions within the semantic space to enhance the performance and
selection efficacy of multi-expert models. Utilizing this framework, we trained
a financial multi-task LLM named "SilverSight". Our research findings
demonstrate that our framework can achieve results close to those obtained with
full data training using only 10% of the data, while also exhibiting strong
generalization capabilities.
