인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In common data environment (CDE) systems, design information is retrieved using structured query formulas, which require knowledge of data structures and query syntax. This increases user burden and limits effective utilization of accumulated design data. Recent advances in large language models (LLMs) have shown potential for generating structured search formulas from natural language input; however, their applicability in real industrial design environments remains insufficiently examined. This study investigates the feasibility and limitations of natural language–based design query generation using LLMs under a closed design data environment with restricted external network access. A system was implemented to convert natural language queries into XML-based design search formulas using a locally deployed LLM, and experiments were conducted to analyze the structural characteristics of the generated queries. The experimental results show that the LLM can generate the basic structure of XML design queries with reasonable consistency, and that expanding the training data and iterative fine-tuning improve structural stability. However, limitations were identified in handling complex query conditions, suggesting the need for additional validation and refinement mechanisms. This study offers practical insights into the realistic use of LLMs for design data retrieval in CDE environments.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-26-02-096364237
