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Paper Abstract and Keywords
Presentation 2024-09-03 09:20
Automatic Generation Method of Definition Statements for Binary Classification of Texts Using Large Language Models
Kaito Takano, Kei Nakagawa, Yugo Fujimoto (NAM) NLC2024-14
Abstract (in Japanese) (See Japanese page) 
(in English) Text classification using large language models~(LLMs) often has low interpretability and is hard to adjust manually. On the other hand, zero-shot learning, which uses definition statements in LLMs for classification, is more interpretable but creating good definition statements is a challenging task.
Therfore, we propose a method to automatically generate definition statements using LLMs to improve classification accuracy and interpretability in (binary) text classification.
The proposed method first randomly splits the labeled data and generates (initial) definition statements based on sampled data. Then, it classifies the labeled data using these statements and updates them by inputting misclassified data back into LLMs, repeating this process to improve the definition statements.
Experiments with real-world texts show that the proposed method performs well compared to fine-tuned BERT model and LLM few-shot learning and creates appropriate definition sentences.
Keyword (in Japanese) (See Japanese page) 
(in English) large language models / prompt engineering / zero-shot learning / few-shot learning / BERT / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 173, NLC2024-14, pp. 75-80, Sept. 2024.
Paper # NLC2024-14 
Date of Issue 2024-08-26 (NLC) 
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 NLC2024-14

Conference Information
Committee NLC  
Conference Date 2024-09-02 - 2024-09-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido University. The Clark Memorial Student Center. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 21st Text Analytics Symposium 
Paper Information
Registration To NLC 
Conference Code 2024-09-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic Generation Method of Definition Statements for Binary Classification of Texts Using Large Language Models 
Sub Title (in English)  
Keyword(1) large language models  
Keyword(2) prompt engineering  
Keyword(3) zero-shot learning  
Keyword(4) few-shot learning  
Keyword(5) BERT  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kaito Takano  
1st Author's Affiliation Nomura Asset Management Co., Ltd. (NAM)
2nd Author's Name Kei Nakagawa  
2nd Author's Affiliation Nomura Asset Management Co., Ltd. (NAM)
3rd Author's Name Yugo Fujimoto  
3rd Author's Affiliation Nomura Asset Management Co., Ltd. (NAM)
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Speaker Author-1 
Date Time 2024-09-03 09:20:00 
Presentation Time 20 minutes 
Registration for NLC 
Paper # NLC2024-14 
Volume (vol) vol.124 
Number (no) no.173 
Page pp.75-80 
#Pages 6 
Date of Issue 2024-08-26 (NLC) 

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