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This paper introduces an evaluation suite for testing whether language models understand Korean cultural context, using news, idioms, and poetry at the word, sentence, and paragraph levels. Its core argument is that benchmarks built by translating English data can carry Western cultural assumptions, so they may overstate or misstate a model's competence in other languages. For AI readers, the main takeaway is that the authors report substantial gaps in deeper cultural understanding, and that evaluation design, not just model scale, shapes what looks like progress.
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KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2412.07251v1 Announce Type: cross Abstract: Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, we introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture that features datasets of cultural news, idioms, and poetry. It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels. Using the KULTURE Bench, we assessed the capabilities of models trained with different language corpora and analyzed the results comprehensively. The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture.

Abstract:Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, we introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture that features datasets of cultural news, idioms, and poetry. It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels. Using the KULTURE Bench, we assessed the capabilities of models trained with different language corpora and analyzed the results comprehensively. The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2412.07251 [cs.CL]

(or arXiv:2412.07251v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2412.07251 Focus to learn more arXiv-issued DOI via DataCite

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