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.

翻译：大语言模型（LLMs）正日益广泛应用于各个专业领域，依托其丰富的知识赋能众多场景。然而，每个领域都包含多种需要学习的特定任务，且这些领域中多样化的异质数据可能导致模型任务迁移时的冲突。针对这一挑战，我们的研究引入了一种自适应语义空间学习（ASSL）框架，该框架通过语义空间中数据分布的自适应重组来提升多专家模型的性能与选择效率。利用该框架，我们训练了一个名为“SilverSight”的金融多任务大语言模型。研究结果表明，我们的框架仅需使用10%的数据即可达到接近全数据训练的结果，同时展现出强大的泛化能力。