ELTEX: A Framework for Domain-Driven Synthetic Data Generation
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.
67db9586a2f164ac51f84c72
Arina Razmyslovich
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2025-03-20T04:13:32.101Z
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Arina Razmyslovich
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lavriz
67db9586a2f164ac51f84c73
Kseniia Murasheva
admin_assigned
2025-03-20T13:29:50.922Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/OBfFNaBuLyuEEuuszJfHj.png
Kseniia Murasheva
user
Kseniamorph
67db9586a2f164ac51f84c74
Sofia Sedlova
67db9586a2f164ac51f84c75
Julien Capitaine
admin_assigned
2025-03-20T13:30:01.959Z
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/avatars/3c636ed07e42c9883b0f7ad70b74f156.svg
Julien Capitaine
user
DrKapichu
67db9586a2f164ac51f84c76
Eugene Dmitriev
ELTEX: A Framework for Domain-Driven Synthetic Data Generation
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.
ELTEX, a domain-driven framework for generating synthetic training data, enhances LLM performance in specialized domains like cybersecurity through systematic domain indicator extraction and dynamic prompting, using fewer computational resources.
Large Language Models (LLMs) domain-driven framework ELTEX efficient LLM token extraction domain-specific training data blockchain-related cyberattack detection Gemma-2B GPT-4 standard classification metrics uncertainty calibration synthetic dataset