模态框(Modal)标题

在这里添加一些文本

模态框(Modal)标题

  • Home
  • About Journal
    • Basic Information
    • Copyright Transfer Agreement
    • Open Access Policy
    • Copyright and Archive Policy
  • Journal Browsing
    • Table of Contents
    • Online First
    • Archived Issues
  • Editorial Board
  • Author Center
    • Author Guidelines
    • Publication Ethics
    • Policies on Academic Dishonesty
    • Editorial Policy
  • Download
  • Subscription
  • Contact Us
  • 中文
Search
E-mail
RSS

Quice Search

Figure/Table detail

LEAD-Cyber:a local fine-tuned vertical domain LLMs for cybersecurity with open-source LLMs and full-cycle training datasets
GUAN Yongjian, PIAO Chengkai, WANG Buhong, ZHAO Bofu, LI Siqi, ZHAO Zhengyang
Journal of Cybersecurity, DOI: 10.20172/j.issn.2097-3136.250116

Fig.5 Rouge-L experimental results
Other figure/table from this article
  • Fig.1 The overview of LEAD-Cyber.
  • Table 1 Pre-training Dataset statistics
  • Table 2 Instruction Fine-tune Dataset statistics
  • Table 3 Reasoning Fine-tune Dataset statistics
  • Table 4 CTI-Bench Reasoning Task
  • Table 5 HackMentor Dataset statistics
  • Fig.2 Example of Q&A for Data Generation
  • Fig.3 Schematic diagram of LoRA
  • Fig.4 Experimental results the test dataset
  • Fig.6 Qwen score diagram of LEAD
  • Fig.7 WinRate of each combination of models.
  • Fig.8 Experimental results on CTI-Bench
  • Fig.9 Rouge-L on experimental results on CTI-Bench
  • Fig.10 Qwen score diagram of CTI-Bench
  • Fig.11 WinRate of each combination of models.
  • Fig.12 Experimental results on HackMentor-eval
  • Fig.13 Rouge-L on experimental results
  • Fig.14 Qwen score diagram of HackMentor-eval
  • Fig.15 WinRate of each combination of models.
  • Links:
  • China Aerospace Science and Technology Corporation
Website Copyright © Editorial Office of Journal of Cybersecurity.
Tel: 010-89061756/89061778 
E-mail:wlkjaqkxxb@spacechina.com
Powered by Beijing Magtech Co. Ltd,.

京ICP备16032046号-20