Korean Generative Pre-trained Transformer
| dc.contributor.author | Yang, Kichang | |
| dc.date.accessioned | 2025-06-02T13:40:52Z | |
| dc.date.available | 2025-06-02T13:05:39Z | |
| dc.date.available | 2025-06-02T13:40:52Z | |
| dc.date.issued | 2021-12-06 | |
| dc.description | KoGPT는 한국어 언어 특성에 최적화된 생성형 언어모델로, 한국어 자연어처리(NLP) 태스크에서의 성능 향상을 목표로 개발되었습니다. ©2021 NAVER AI Lab | |
| dc.description.abstract | With the advent of Transformer, which was used in translation models in 2017, attention-based architectures began to attract attention. Furthermore, after the emergence of BERT, which strengthened the NLU-specific encoder part, which is a part of the Transformer, and the GPT architecture, which strengthened the NLG-specific decoder part, various methodologies, data, and models for learning the Pretrained Language Model began to appear. Furthermore, in the past three years, various Pretrained Language Models specialized for Korean have appeared. In this paper, we intend to numerically and qualitatively compare and analyze various Korean PLMs released to the public. | |
| dc.description.sponsorship | NAVER AI Lab | |
| dc.identifier.issn | https://arxiv.org/abs/2112.03014 | |
| dc.identifier.uri | http://data.inu.ac.kr/handle/123456789/1953.2 | |
| dc.language.iso | en_US | |
| dc.publisher | arXiv | |
| dc.subject | KoGPT | |
| dc.subject | Korean NLP | |
| dc.subject | LLM | |
| dc.subject | Language Model | |
| dc.subject | Generation | |
| dc.title | Korean Generative Pre-trained Transformer | |
| dc.type | Article |