ELTEX: A Framework for Domain-Driven Synthetic Data Generation
Authors: name: Arina Razmyslovich, name: Kseniia Murasheva, name: Sofia Sedlova, name: Julien Capitaine, name: Eugene Dmitriev

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.