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
In recent years, notable progress in natural language processing (NLP) has been attributed to the advent of large language models (LLM), exemplified by innovations such as chat generative pre-trained transformer (ChatGPT) developed by OpenAI. Complementary LLMs such as Llama and Vicuna, are also being rapidly developed. Despite these advancements, LLMs still have limitations because of their training on generic text sources like Wikipedia. While some LLMs are trained on data spanning multiple domains, they often exhibit suboptimal performance in specialized domains like chemistry or finance. This study aims to address this limitation by developing an LLM specialized for the chemical domain through instruction tuning. We use a diverse dataset in both Korean and English, encompassing multiple tasks such as summarization, keyword extraction, and question answering for instruction tuning. The baseline model for our study is Llama2. Evaluation using both Korean and English datasets demonstrates that our specialized Llama2 outperforms its untuned counterpart in both languages. This demonstrates the effectiveness of our approach in enhancing the performance of models across languages and domains, particularly in chemistry. Additionally, we propose efficient instruction tuning strategies through various experiments.
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) : I410-151-25-02-092745679
