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VLM-Based Context Extraction and Fine-Tuning of Korean LLMs for Automatic Travel Blog Content Generation

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(6), pp.77~90
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 16, 2025
  • Accepted : June 9, 2025
  • Published : June 30, 2025

Donghun Lim 1 Seungsoo Han 2 Euichan Eun 3 Dongyeong Kim 4 Janghoon Choi 1

1경북대학교
2(주)펀진
3한양대학교
4울산대학교

Accredited

ABSTRACT

In this paper, we propose an automated system for generating Korean travel blog content by integrating Vision-Language Model (VLM)–based context extraction, fine-tuned Korean language model, and a text-to-image (T2I) generation model via prompt engineering. Using the Qwen2-VL model, we analyze travel photos to extract visual context and produce emotionally nuanced captions. We then leverage a large-scale crawled corpus of real-world travel blogs to fine-tune HyperCLOVA X, enabling it to create natural, storytelling-oriented blog text. In addition, we employ a travel-specific prompt engineering approach with DALL-E to generate custom postcards and stamp images, providing users with intuitive and creative value for their travel records. A robust system prompt minimizes hallucinations while preserving expressive writing. User surveys indicate that the fine-tuned model is, on average, 60.9% more specialized in travel-related content. These findings demonstrate that the proposed approach can significantly reduce manual effort while producing high-quality travel blog content, suggesting new possibilities for AI-based content creation in the tourism domain.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.