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arXiv — NLP / Computation & Language · · 3 min read

KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs

#reasoning #paper #benchmark

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Computer Science > Computation and Language

arXiv:2510.18368 (cs)
[Submitted on 21 Oct 2025 (v1), last revised 11 Sep 2026 (this version, v2)]

Title:KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs

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Abstract:We present $\textbf{Korean SimpleQA (KoSimpleQA)}$, a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is designed to be challenging yet easy to grade, consisting of 938 short, fact-seeking questions with unambiguous answers. We conduct a comprehensive evaluation across a diverse set of open-source LLMs of varying sizes that support Korean, and find that even the strongest model generates correct answer only 31.6% of the time, underscoring the challenging nature of KoSimpleQA. Notably, performance rankings on KoSimpleQA differ substantially from those on the English SimpleQA, highlighting the unique value of our dataset. Furthermore, we observe that reasoning helps mitigate the cross-lingual knowledge gap in LLMs, which refers to disparities in their ability to manifest knowledge across languages. KoSimpleQA can be found at this https URL.
Comments: Accepted at COLM 2026. Camera-ready version
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2510.18368 [cs.CL]
  (or arXiv:2510.18368v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2510.18368
arXiv-issued DOI via DataCite

Submission history

From: Donghyeon Ko [view email]
[v1] Tue, 21 Oct 2025 07:37:51 UTC (995 KB)
[v2] Fri, 11 Sep 2026 05:53:21 UTC (1,294 KB)
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