title: ELTEX: A Framework for Domain-Driven Synthetic Data Generation
summary: 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.
paper_authors._id: 67db9586a2f164ac51f84c72
paper_authors.name: Arina Razmyslovich
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-20T04:13:32.101Z
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paper_authors.avatarUrl: /avatars/4efb005ce798ce82d4053e00e0ba9f23.svg
paper_authors.fullname: Arina Razmyslovich
paper_authors.type: user
paper_authors.user: lavriz
paper_authors._id: 67db9586a2f164ac51f84c73
paper_authors.name: Kseniia Murasheva
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T13:29:50.922Z
paper_authors._id: 6786af3f8c6e6a8a0fb39b45
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/OBfFNaBuLyuEEuuszJfHj.png
paper_authors.fullname: Kseniia Murasheva
paper_authors.type: user
paper_authors.user: Kseniamorph
paper_authors._id: 67db9586a2f164ac51f84c74
paper_authors.name: Sofia Sedlova
paper_authors._id: 67db9586a2f164ac51f84c75
paper_authors.name: Julien Capitaine
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T13:30:01.959Z
paper_authors._id: 64aab6e5d4d43713ceb83fb1
paper_authors.avatarUrl: /avatars/3c636ed07e42c9883b0f7ad70b74f156.svg
paper_authors.fullname: Julien Capitaine
paper_authors.type: user
paper_authors.user: DrKapichu
paper_authors._id: 67db9586a2f164ac51f84c76
paper_authors.name: Eugene Dmitriev
paper_title: ELTEX: A Framework for Domain-Driven Synthetic Data Generation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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