## 7 Conclusions and Future Work  
This study proposes a novel benchmark for evaluating the cultural understanding and natural language processing capabilities of large language models, grounded in Hanja and four-character idioms, which are significant linguistic assets in Korean culture. Reflecting the official question types used in Korea's Hanja Proficiency Test, the benchmark includes four question types: four-character idioms, synonyms, antonyms, and homophones. Based on this framework, the study systematically compares and analyzes the performance of both GPT-based and non-GPT-based large language models.  
The experimental results indicate that GPT-4o achieved the highest accuracy and explanation quality across all four question types. This suggests the model's potential to comprehend the nuanced meanings of Korean Sino-Korean words and four-character idioms, as well as interpret their cultural contexts. The model demonstrated particularly strong performance in antonym and homophone questions, and the four-character idiom tasks enabled an assessment of how large language models infer metaphorical expressions. However, difficulties remain in distinguishing the subtle semantic nuances of individual Hanja characters. GPT-4o often relied on the primary dictionary definitions of Hanja and tended to interpret them according to Chinese usages rather than meanings specific to Korean society.  
This study reveals the current limitations in large language models' understanding of Hanja as used in Korean contexts, underscoring the need for evaluation tools that reflect the uniquely Korean interpretations of Hanja, distinct from Chinese conventions. To develop models that accurately represent individual cultures and languages, including Korean, it is essential to build datasets that reflect the culturally embedded meanings of Korean Hanja.  
Moreover, collaborative frameworks that incorporate multiple tools--such as compound systems--should be actively adopted.  
By constructing a benchmark based on Korean-Hanja pairs rather than a single language, this study introduces a new approach to evaluating cultural comprehension in large language models through Sino-Korean words and four-character idioms. This research contributes to advancing performance evaluation from a purely quantitative focus to a more qualitative evaluation that incorporates cultural context, bridging the fields of humanities and natural language processing. Ultimately, it aims to serve as a foundation for global benchmarking efforts that assess the linguistic and cultural competencies of large language models in non-English languages, including Korean.  
Conceptualization, E.L. and D.O.; methodology, D.O.; software, E.L.; validation, H.D. and M.K.; formal analysis, E.L. and M.K.; investigation, E.L., H.D., and M.K.; resources, E.L., H.D., and M.K.; data curation, E.L., H.D. and M.K.; writing--original draft preparation, E.L.; writing--review and editing, E.L. and D.O.; visualization, E.L.; supervision, D.O.; project administration, E.L. and D.O.; funding acquisition, None. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
Not applicable  
We constructed the dataset using the Hanja entries from the textbook titled \"Hanja Proficiency Test Level 1\" (), which has been designated by the Korean Literary Association as the official guidebook for exam preparation. Upon request, we will share the analyzed data.  
The authors declare no conflicts of interest.