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.
피인용 0
초록·키워드
As large language models (LLMs) continue to improve in performance, efforts to apply them for practical use across various domains have intensified. However, challenges remain due to the inherent variability in LLM responses and the issue of hallucinations, necessitating systematic management approaches. These problems are particularly pronounced in the medical domain, where such systems have yet to gain the full trust of domain experts. In this study, we propose a self-confidence score based selective response process to address these issues. This process combines selective responding with the metacognitive metrics of LLMs, filtering only those responses with high confidence scores. Furthermore, by iteratively applying this process, we demonstrate improvements in the coverage–accuracy trade-off. On the KorMedMCQA dataset, our method achieved an accuracy of 90.85% and a coverage of 71.11% after four iterations. Compared to the non-iterative setting, accuracy decreased by 2.2%, but coverage increased by 82.5%. These results indicate that leveraging LLMs’ metacognitive cues can improve the trade-off between coverage and accuracy. Furthermore, this approach demonstrates practical applicability in the medical domain and shows the contribution of metacognition based methods to enhance reliability.
본문·목차
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UCI(KEPA) : I410-151-25-02-094042591
