DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 노용만 | - |
dc.contributor.author | Kim, Chae Won | - |
dc.contributor.author | 김채원 | - |
dc.date.accessioned | 2024-07-25T19:31:12Z | - |
dc.date.available | 2024-07-25T19:31:12Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045896&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320667 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2023.8,[iii, 19 p. :] | - |
dc.description.abstract | Open-Domain Dialogue (ODD) systems present unique challenges in generating engaging conversations such as contextual understanding and generating engaging and natural responses. With the recent remarkable progress of Large Language Models (LLM), we aim to enhance smaller dialogue models by extracting emotion knowledge from LLMs. We introduce the concept of Emotion-aware Memory (EM), which acts as a repository to store key emotional features extracted from LLMs. By leveraging EM during the response decoding stage, we aim to generate emotionally-enriched responses. We experiment and achieve state-of-the-art performance on DailyDialog and EmpatheticDialogues datasets. This research contributes to emotionally-aware dialogue systems and paves the way for more engaging conversational agents. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 오픈 도메인 대화▼a공감 응답 생성▼a대규모 언어 모델▼a지식 추출 | - |
dc.subject | Open-domain dialogue▼aEmpathetic response generation▼aLarge language models▼aKnowledge extraction | - |
dc.title | Enhancing dialogue response generation through emotion memory | - |
dc.title.alternative | 감정 메모리를 활용한 대화 응답 생성 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | Ro, Yong Man | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.