An Intelligent-Aware Transformer with Domain Adaptation and Contextual Reasoning for Question Answering

With the rapid growth of financial data, extracting accurate and contextually relevant information remains a challenge. Existing financial question-answering (QA) models struggle with domain-specific terminology, long-document processing, and answer consistency. To address these issues, this paper proposes the Intelligent-Aware Transformer (IAT), a financial QA system based on GLM4-9B-Chat, integrating a multi-level information aggregation framework. The system employs a Financial-Specific Attention Mechanism (FSAM) to enhance focus on key financial terms, a Dynamic Context Embedding Layer (DCEL) to improve long-document processing, and a Hierarchical Answer Aggregator (HAA) to ensure response coherence. Additionally, Knowledge-Augmented Textual Entailment (KATE) strengthens the model's generalization by inferring implicit financial knowledge. Experimental results demonstrate that IAT surpasses existing models in financial QA tasks, exhibiting superior adaptability in long-text comprehension and domain-specific reasoning. Future work will explore computational optimizations, advanced knowledge integration, and broader financial applications.