An Augmentable Domain-specific Models for Financial Analysis

Large-scale language models such as GPT-4 have revolutionized data analysis and interpretation by generating human-like text, automating insights, and detecting data errors. Large-scale language models have been applied in various fields and played an important role in many aspects. Large language models can also perform financial and technical analysis by cleaning data, generating synthetic data, handling bias, and supporting natural language queries. This paper proposes a language model that integrates multimodal data with external knowledge bases and domain-specific data, enhancing its reasoning ability by extending domain-specific data. Reduce hallucinations and fine-tune domain-specific data by incorporating external knowledge bases to deepen model understanding of industry-specific language, concepts, and context. And technologies such as knowledge graph, attention mechanism, cross-modal embedding and federated collaborative training are used to deal with the challenges of different structures and semantics of multi-modal data. The model also employs a feedback loop mechanism to allow the model to adapt to changing conditions, such as changing languages or new domain information. Experimental results show that the proposed model has a preliminary domain-specific ability to analyze and predict multimodal financial and technical data.