mlsanityData to 13 Oct 2026

ELTEX: A Framework for Domain-Driven Synthetic Data Generation

arXiv 2503.15055 ↗First posted 19 Mar 2025cs.CLcited by 2

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

We introduce Efficient LLM Token Extraction (ELTEX), a framework addressing the critical challenge of LLM domain specialization by systematically extracting and integrating domain indicators throughout synthetic data generation. Unlike approaches relying on implicit knowledge transfer, ELTEX explicitly leverages domain signals to maintain specialized knowledge integrity. In our cybersecurity case study, ELTEX-enhanced data enables a fine-tuned Gemma-2B model to achieve performance competitive with GPT-4o on blockchain cyberattack classification while reducing computational requirements. Our Google Sheets implementation makes ELTEX accessible to non-technical users. Our contributions include: (1) the ELTEX framework; (2) Google Sheets Add-on implementation; (3) empirical validation showing how ELTEX bridges performance gaps between small and large models; and (4) a synthetic dataset of 11,448 texts for blockchain cyberattack detection.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 9 of its 25 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

9 of 25 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation3 Sep
  2. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey14 Jun

Other references · 7

  1. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,197
  2. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,908
  3. 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 486
  4. 2020Learning under Concept Drift: A Reviewcited by 474
  5. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 135
  6. 2024Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiescited by 68
  7. 2025Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Trainingcited by 23

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructionscited by 3
  2. 2026From Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework
  2. 2024Blockchain Data Analysis in the Era of Large-Language Models
  3. 2026CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?
  4. 2025ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detection
  5. 2026CryptoAnalystBench: Failures in Multi-Tool Long-Form LLM Analysis
  6. 2024Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities
  7. 2026Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications
  8. 2026When Verified Source Becomes Attack Input: Defending Smart Contracts Against LLM-Based Vulnerability Scanning ↗