Earticle

초록

영어

While conventional LLMs provide uniform services to various users, Personal LLMs can improve user experience by optimizing for the needs and environments of specific users. The purpose of this paper is to identify how the personalized features of Personal LLM can improve user satisfaction and efficiency, and the challenges associated with its application. The results of the study show that Personal LLM significantly improves work efficiency by providing customized responses and reflecting the specific needs of users. In addition, LLM showed progressively better performance over time through learning, and it was confirmed that it can be gradually improved through interaction with users. However, it was confirmed that there are technical and ethical limitations, such as data privacy issues, which remain important challenges in commercializing Personal LLM. This paper suggests the possibility that Personal LLM can provide customized services to users and provides important basic data for the development of personalized AI systems in the future.

목차

Abstract
1. Introduction
2. General LLM vs. Personal LLM
3. Design and Structure of Personal LLM
3.1 Design Elements
3.2 Structural Features
4. Advantages and Limitations of Personal LLM
4.1 Advantages of Personal LLM
4.2 Limitations of Personal LLM
5. Conclusion
References

키워드

저자

• Seok-Hyang Cho [ Professor, Dept. of Information & Communication, Pyeongtaek University ]
• Yo-Seob Lee [ Professor, Dept. of Smart Contents, Pyeongtaek University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380