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Paper Abstract and Keywords
Presentation 2024-11-15 10:25
Assessing the Impact of Behavior Change Messages Using Large Language Models
Takuya Okada (YCU), Yoshiaki Takimoto, Takeshi Kurashima (NTT), Hiroyuki Toda (YCU) ISEC2024-77 SITE2024-74 LOIS2024-41
Abstract (in Japanese) (See Japanese page) 
(in English) Lifestyle-related diseases are a serious problem in modern society, and one of the proposed solutions involves intervention studies using messages generated by large language models(LLMs). In conventional research, the evaluation of messages generated by LLMs was typically conducted by experts or through crowdsourcing. While such human feedback is essential for generating better messages, it presents issues in terms of time and cost. Therefore, this study aims to develop a model for predicting the effectiveness of behavior change messages, working towards the realization of an LLM-based message generation framework that minimizes the need for human evaluation. Specifically, we propose a model to predict the effectiveness of messages using survey data where behavior change messages were evaluated manually. The proposed method showed an 11% improvement in prediction accuracy compared to conventional supervised learning methods using Random Forest.
Keyword (in Japanese) (See Japanese page) 
(in English) Healthcare / Behavior Change / Personalization / Large Language Models / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 257, LOIS2024-41, pp. 101-106, Nov. 2024.
Paper # LOIS2024-41 
Date of Issue 2024-11-07 (ISEC, SITE, LOIS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF ISEC2024-77 SITE2024-74 LOIS2024-41

Conference Information
Committee SITE LOIS ISEC  
Conference Date 2024-11-14 - 2024-11-15 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To LOIS 
Conference Code 2024-11-SITE-LOIS-ISEC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Assessing the Impact of Behavior Change Messages Using Large Language Models 
Sub Title (in English)  
Keyword(1) Healthcare  
Keyword(2) Behavior Change  
Keyword(3) Personalization  
Keyword(4) Large Language Models  
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1st Author's Name Takuya Okada  
1st Author's Affiliation Yokohama City University (YCU)
2nd Author's Name Yoshiaki Takimoto  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Takeshi Kurashima  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Hiroyuki Toda  
4th Author's Affiliation Yokohama City University (YCU)
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Speaker Author-1 
Date Time 2024-11-15 10:25:00 
Presentation Time 25 minutes 
Registration for LOIS 
Paper # ISEC2024-77, SITE2024-74, LOIS2024-41 
Volume (vol) vol.124 
Number (no) no.255(ISEC), no.256(SITE), no.257(LOIS) 
Page pp.101-106 
#Pages 6 
Date of Issue 2024-11-07 (ISEC, SITE, LOIS) 

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