Reference Aware Delexicalization (RAD) Framework: Theory Driven Artificial Intelligence Modeling for Domain Generalization
提出参考感知去词汇化（RAD）框架，通过抽象领域特定术语并保留语义关系，使AI模型聚焦于可跨领域泛化的逻辑结构，无需大量计算资源即可提升新场景下的表现，对医疗、金融、社交媒体等领域的实践者和政策制定者有参考价值。
Artificial intelligence systems often underperform in new contexts, limiting their reliability in business domains. This study introduces the reference aware delexicalization (RAD) framework, a theory-driven approach that improves artificial intelligence (AI) model performance across different domains without requiring massive computational resources. RAD addresses a fundamental problem: AI models often memorize surface patterns from training data rather than learning transferable reasoning skills. By systematically abstracting domain-specific terms while preserving semantic relationships, RAD enables models to focus on underlying logical structures that generalize across contexts. For practitioners, RAD offers measurable benefits. Healthcare organizations can deploy clinical decision support systems that maintain accuracy when processing records from different departments or institutions. Financial institutions can build fraud detection systems that adapt to emerging threats without extensive retraining. Social media platforms can improve content moderation consistency across languages and cultural contexts. For policymakers, RAD demonstrates that effective AI does not require ever-larger models with corresponding environmental and economic costs. Organizations can achieve robust, adaptable AI systems through principled data augmentation techniques. This finding supports policies encouraging efficient, interpretable AI development over resource-intensive scaling approaches, promoting both technological sustainability and broader access to reliable AI capabilities.