In vast information fields like engineering investment and international trade, **accurately extracting core project elements** (e.g., project name, investment amount, executing agency) is crucial for corporate decision-making. Traditional methods rely on supervised learning, while the advent of large models is revolutionizing this domain. Based on real business scenarios undertaken by our company, data annotation costs, technical implementation paths, and case comparisons, this article provides a deep analysis of the differences and selection strategies between these two technical routes.
Practical construction of large models in the vertical domain of audit knowledge. In the wave of digital transformation, the audit industry is undergoing intelligent changes. Our **audit-specific AI assistant**, developed by fine-tuning large language models, has successfully increased audit phenomenon recognition efficiency by 200% and improved problem identification accuracy to over 90%. This article reveals the secrets behind this AI-enabled innovative practice.
Through pre-training and fine-tuning, our AI team has constructed a large model specialized in state-owned enterprise knowledge. From 2024 to the first half of 2025, we conducted two rounds of vertical domain training. Evaluation metrics have surpassed those of the base model and meet user requirements.