Discovering inconsistencies in documents with long-context LLMs
Loading...
Author (Corporation)
Publication date
2025
Type of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
Advanced Information Systems Engineering Workshops. CAiSE 2025 Workshops, Vienna, Austria, June 16–20, 2025, Proceedings
Special issue
DOI of the original publication
Link
Related research data
Series
Series number
Volume
Issue / Number
Pages / Duration
105-116
Patent number
Publisher / Publishing institution
Springer
Place of publication / Event location
Cham
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
The increasing complexity and scale of technical document corpora present challenges for consistency verification, particularly in politically sensitive or high-stakes contexts. This paper proposes an iterative approach that integrates long-context large language models (LLMs), human expertise, and hybrid clustering mechanisms to address these challenges. The approach focuses on two types of inconsistencies: real inconsistencies, such as contradictory statements or omissions, and fabricated inconsistencies, which are plausible yet artificially introduced.This paper uses the Swiss National Cooperative for the Disposal of Radioactive Waste (Nagra) and its corpus of up to 300 technical documents as a case study. Experimental results suggest that targeted structuring of document contexts improves recall in inconsistency detection. The findings highlight the potential of combining structured human input with LLM-based reasoning for improving document integrity and trustworthiness. Future work will focus on refining the approach, including automated clustering strategies and optimization of prompt engineering.
Keywords
Subject (DDC)
Event
International Conference on Advanced Information Systems Engineering
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
978-3-031-94931-9
978-3-031-94930-2
978-3-031-94930-2
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Closed
License
Citation
Martin, A., Witschel, H. F., Stockhecke, M., Nydegger, P., & Buga, K. (2025). Discovering inconsistencies in documents with long-context LLMs. In J. Grabis & Y. Wautelet (Eds.), Advanced Information Systems Engineering Workshops. CAiSE 2025 Workshops, Vienna, Austria, June 16–20, 2025, Proceedings (pp. 105–116). Springer. https://doi.org/10.1007/978-3-031-94931-9_9