Hermit Kingdom Through the Lens of Multiple Perspectives: A Case Study of LLM Hallucination on North Korea
Eunjung Cho, Won Ik Cho, Soomin Seo

TL;DR
This paper investigates how multilingual large language models generate information about North Korea across different languages, revealing significant variations that impact understanding and security implications.
Contribution
It provides a case study on LLM hallucinations about North Korea, highlighting the influence of model choice and language on information accuracy in sensitive geopolitical contexts.
Findings
Model and language choice significantly affect information accuracy.
Different models produce vastly different understandings of North Korea.
Implications for security and misinformation in geopolitical scenarios.
Abstract
Hallucination in large language models (LLMs) remains a significant challenge for their safe deployment, particularly due to its potential to spread misinformation. Most existing solutions address this challenge by focusing on aligning the models with credible sources or by improving how models communicate their confidence (or lack thereof) in their outputs. While these measures may be effective in most contexts, they may fall short in scenarios requiring more nuanced approaches, especially in situations where access to accurate data is limited or determining credible sources is challenging. In this study, we take North Korea - a country characterised by an extreme lack of reliable sources and the prevalence of sensationalist falsehoods - as a case study. We explore and evaluate how some of the best-performing multilingual LLMs and specific language-based models generate information…
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Taxonomy
TopicsKorean Peninsula Historical and Political Studies
