ELTEX: A Framework for Domain-Driven Synthetic Data Generation
Arina Razmyslovich, Kseniia Murasheva, Sofia Sedlova, Julien Capitaine, Eugene Dmitriev
2025-03-20
Summary
This paper discusses a way to create fake training data that's really good for teaching AI about specific topics, like cybersecurity.
What's the problem?
AI models often struggle with specialized areas because there isn't enough real data to train them on.
What's the solution?
The researchers created a system called ELTEX that uses AI to generate realistic fake data that includes important details about the specific topic.
Why it matters?
This work matters because it can help AI learn about complex topics even when there isn't much real-world data available.
Abstract
We present ELTEX (Efficient LLM Token Extraction), a domain-driven framework for generating high-quality synthetic training data in specialized domains. While Large Language Models (LLMs) have shown impressive general capabilities, their performance in specialized domains like cybersecurity remains limited by the scarcity of domain-specific training data. ELTEX addresses this challenge by systematically integrating explicit domain indicator extraction with dynamic prompting to preserve critical domain knowledge throughout the generation process. We demonstrate ELTEX's effectiveness in the context of blockchain-related cyberattack detection, where we fine-tune Gemma-2B using various combinations of real and ELTEX-generated data. Our results show that the ELTEX-enhanced model achieves performance competitive with GPT-4 across both standard classification metrics and uncertainty calibration, while requiring significantly fewer computational resources. We release a curated synthetic dataset of social media texts for cyberattack detection in blockchain. Our work demonstrates that domain-driven synthetic data generation can effectively bridge the performance gap between resource-efficient models and larger architectures in specialized domains.