FlexiDataGen: An Adaptive LLM Framework for Dynamic Semantic Dataset Generation in Sensitive Domains

2025-10-21

AI Summary

Critical domains—including healthcare, biomedicine, and cybersecurity—face persistent challenges of data scarcity, high annotation costs, and stringent privacy regulations. Method: We propose an adaptive, large language model–driven synthetic data generation framework that integrates retrieval-augmented generation (RAG), fine-grained syntactic-semantic analysis, dynamic domain-knowledge injection, and multi-round semantic consistency verification to establish a closed-loop generation and iterative optimization pipeline. Contribution/Results: Unlike static template–based or opaque generative approaches, our framework enables the first semantic-controllable, domain-credible, and linguistically diverse synthetic data generation tailored to high-stakes applications. Empirical evaluation demonstrates substantial alleviation of annotation bottlenecks and consistent improvements in model accuracy and generalization across multiple downstream tasks. The framework establishes a scalable, verifiable paradigm for trustworthy AI development in privacy-sensitive domains.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Dataset availability and quality remain critical challenges in machine learning, especially in domains where data are scarce, expensive to acquire, or constrained by privacy regulations. Fields such as healthcare, biomedical research, and cybersecurity frequently encounter high data acquisition costs, limited access to annotated data, and the rarity or sensitivity of key events. These issues-collectively referred to as the dataset challenge-hinder the development of accurate and generalizable machine learning models in such high-stakes domains. To address this, we introduce FlexiDataGen, an adaptive large language model (LLM) framework designed for dynamic semantic dataset generation in sensitive domains. FlexiDataGen autonomously synthesizes rich, semantically coherent, and linguistically diverse datasets tailored to specialized fields. The framework integrates four core components: (1) syntactic-semantic analysis, (2) retrieval-augmented generation, (3) dynamic element injection, and (4) iterative paraphrasing with semantic validation. Together, these components ensure the generation of high-quality, domain-relevant data. Experimental results show that FlexiDataGen effectively alleviates data shortages and annotation bottlenecks, enabling scalable and accurate machine learning model development.

Problem

• Addresses data scarcity in sensitive domains like healthcare
• Overcomes high costs and privacy constraints in data acquisition
• Generates semantically coherent datasets to improve model accuracy

Innovation

• Adaptive LLM framework for semantic dataset generation
• Integrates syntactic-semantic analysis and retrieval-augmented generation
• Uses dynamic element injection and iterative paraphrasing validation