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Evaluation of Geographical Distortions in Language Models

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Rémy Decoupes
Roberto Interdonato
Mathieu Roche
Maguelonne Teisseire
Sarah Valentin
Published in
Discovery Science
Publisher
Springer Nature Switzerland

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Abstract

The chapter 'Evaluation of Geographical Distortions in Language Models' delves into the biases present in large language models (LLMs) that impact their performance in geography-related tasks. It explores how these models, increasingly used through chatbots, perpetuate biases from their training corpora, leading to inaccuracies in geographical knowledge. The study introduces four indicators to assess geographical knowledge reliability and spatial distance distortions in LLMs, evaluating eight commonly used models. The findings reveal significant disparities in geographical knowledge across continents and highlight the need for improved training datasets and model architectures to enhance the reliability of geographical information in LLMs.
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previous chapter Play it Straight: An Intelligent Data Pruning Technique for Green-AI
next chapter AutoML-Guided Fusion of Entity and LLM-Based Representations for Document Classification
Title
Evaluation of Geographical Distortions in Language Models
Authors
Rémy Decoupes
Roberto Interdonato
Mathieu Roche
Maguelonne Teisseire
Sarah Valentin
Copyright Year
2025
Book
Discovery Science

Print ISBN: 978-3-031-78976-2

Electronic ISBN: 978-3-031-78977-9

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-78977-9_6
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