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.

大型语言模型被广泛应用于各个专业领域，本研究引入了一种自适应语义空间学习框架（ASSL），通过对语义空间内数据分布的自适应重组，增强了多专家模型的性能和选择效果。我们使用此框架训练了一个名为 “SilverSight” 的金融多任务 LLM，在只使用 10% 的数据情况下，取得接近完整数据训练的结果，并展现出强大的泛化能力。