## 2 Related Works
### Multilingual Benchmarks and Culturally Specific Data  
Benchmarks are used to evaluate and compare the performance of large language models across a wide range of natural language processing (NLP) tasks, including translation, summarization, question answering, sentiment analysis, and natural language inference (NLI). Particularly, multilingual datasets allow for the measurement of a model's cross-lingual generalization capabilities, highlighting its ability to process linguistic diversity.  
Benchmarks designed to evaluate the cultural understanding of LLMs focus on assessing how well a model processes diverse cultural contexts, social norms, and linguistic nuances. For instance, Massive Multitask Language Understanding includes questionsfrom subjects such as history, geography, and ethics, reflecting cultural differences within its dataset [28]. Similarly, Cross-lingual Natural Language Inference (XNLI) evaluates a model's multilingual inference ability by translating identical sentences into 15 languages, indirectly assessing its contextual understanding across different cultural backgrounds [29]. MEGA [30] aimed to evaluate how well large language models understand languages other than English by using 16 natural language processing datasets. BLEND [31] assessed large language models based on everyday lifestyles across diverse cultures and languages.  
More recently, several visual question answering benchmarks based on multilingual data have been developed, including CVQA [32], CULTURALVQA [33], and GIMMICK [34]. CULTURALVQA revealed that model performance was significantly lower for content related to Islamic and African cultures compared to North American culture. GIMMICK demonstrated that models exhibit a clear bias toward Western culture. These benchmarks consistently report that large language models tend to perform poorly in understanding individual languages and cultures that fall outside high-resource linguistic and cultural contexts.  
There have also been continued efforts to move beyond English-centric evaluation datasets in order to assess models' capabilities in specific linguistic and cultural contexts. ChID [35], for instance, is a Chinese cloze test dataset built using idioms. CIP [36] introduced a task that transforms sentences containing idiomatic expressions into paraphrased sentences that preserve the original meaning but exclude the idioms themselves. Although both studies utilize idioms, they are limited to those found in Chinese, and do not reflect the meanings of idioms commonly used in Korean society.  
Benchmarks such as HAE-RAE Bench [37], CLIcK [38], KoBBQ [39], KoDialogBench [40], and KULTURE Bench [41] have been constructed to evaluate general cultural knowledge related to Korea. KULTURE Bench, in particular, is a framework designed to assess Korean cultural competence and includes datasets of cultural news articles, idioms, and poetry. However, in this dataset, idioms are presented only in a cloze-style reading comprehension format.  
To date, few attempts have been made to construct datasets that accurately reflect the meanings of Sino-Korean words and Sajaseonge, and to evaluate large language models in terms of cultural understanding within the Korean context. Accordingly, this study proposes a benchmark that systematically incorporates Sajaseonge, synonyms, antonyms, and homophones using a parallel data structure of Hanja and Korean. This benchmark aims to conduct an in-depth evaluation of large language models' ability to comprehend Korean cultural contexts. In particular, by focusing on idiomatic expressions that carry metaphorical meaning, the benchmark assesses the extent to which a model understands not merely the sequence of Hanja characters but also their implicit connotations and the underlying cultural background. This approach enables a structured evaluation of the cultural dimensions embedded in Hanja, which are not adequately addressed by existing multilingual benchmarks.