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Paper Abstract and Keywords
Presentation 2026-03-03 11:50
Method for Detecting Dialog Belief Misalignment by LLM-Based Comparison of Belief Graphs
Shingo Muta, Fuminori Nagasawa, Shun Siramatsu (NIT) Consen2025-41
Abstract (in Japanese) (See Japanese page) 
(in English) Misalignment of understanding can degrade decision-making quality, yet its detection still relies heavily on human effort and is often overlooked due to role differences or time constraints. This study proposes an automatic method for detecting recognition misalignment in dialogue using large language models. The proposed approach generates speaker-specific recognition state graphs based on a BDI-inspired model with five node types, and identifies misalignment by extracting structural differences between graphs. Experiments conducted under three conditions showed that while graph structuring did not lead to an overall improvement in discrepancy detection accuracy, the proposed method demonstrated equivalent or higher Precision values, suggesting its potential effectiveness in suppressing false detections. This suggests that while current high-performance LLMs can detect discrepancies with high accuracy using dialogue logs alone, graph structuring may contribute to suppressing false detections.
Keyword (in Japanese) (See Japanese page) 
(in English) Recognition Misalignment / Dialogue Analysis / LLM / Graph visualization / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 377, Consen2025-41, pp. 82-87, March 2026.
Paper # Consen2025-41 
Date of Issue 2026-02-23 (Consen) 
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 Consen2025-41

Conference Information
Committee Consen  
Conference Date 2026-03-02 - 2026-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Research Institute of Electrical Communication, Tohoku University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Co-creative Design and Consensus Design, Disaster Prevention, Multiagent System, etc. 
Paper Information
Registration To Consen 
Conference Code 2026-03-Consen 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Method for Detecting Dialog Belief Misalignment by LLM-Based Comparison of Belief Graphs 
Sub Title (in English)  
Keyword(1) Recognition Misalignment  
Keyword(2) Dialogue Analysis  
Keyword(3) LLM  
Keyword(4) Graph visualization  
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1st Author's Name Shingo Muta  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Fuminori Nagasawa  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Shun Siramatsu  
3rd Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2026-03-03 11:50:00 
Presentation Time 25 minutes 
Registration for Consen 
Paper # Consen2025-41 
Volume (vol) vol.125 
Number (no) no.377 
Page pp.82-87 
#Pages 6 
Date of Issue 2026-02-23 (Consen) 

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