KASTLE: Knowledge-augmented synthetic text generation with logic-enhanced few-shot learning for domain-specific NLP

Domain-specific NLP models critically depend on labelled data that is expensive and scarce in expert fields. While large language models can generate synthetic data, they lack mechanisms to enforce domain-specific factual and logical constraints. This paper introduces KASTLE, a neuro-symbolic framework that integrates domain knowledge graphs directly into the synthetic text generation process through a novel constraint-guided attention mechanism. Unlike existing approaches that apply symbolic reasoning as post-hoc filters, KASTLE embeds domain constraints at every decoding step, enabling context-aware knowledge injection under few-shot conditions. The framework features three components: a knowledge-augmented generator with real-time constraint satisfaction, a multi-layer quality filter assessing syntactic, semantic, domain, and factual dimensions, and a meta-learning engine that dynamically optimises the mixing of synthetic and real data. Extensive evaluation across medical, legal, financial, technical, and scientific domains demonstrates that KASTLE achieves an average F1 Score of 89.5, outperforming the strongest baseline (InstructAug) by 9.1 percentage points and the strongest retrieval-augmented baseline (RAG-Enhanced) by 11.2 percentage points while reducing labelled data requirements by at least 80%.