Application Design and Analysis Method of Dialect Cultural Symbols for Multimodal Large Model
DOI:
https://doi.org/10.64229/ads2025010201Keywords:
multi-modal large model; Dialect cultural symbols; Cross-modal alignment; AIGCAbstract
At present, multimodal large model with the ability to quickly integrate text, image, audio and other multi-dimensional data has become the forefront of the research field. Benefiting from multi-modal technology and AI large model, the design industry has also achieved excellent results. However, the complexity and diversity of cultural symbols in dialects challenge traditional design methods. The integration of multi-modal information and the optimization of creative design with new quality productivity will effectively drive the modern dissemination of dialect cultural symbols. This paper constructs a theoretical framework of multimodal model and dialect culture, covering three levels: cross-modal semantic understanding, generative creation and personalized adaptation. For the first time, this paper applies the multi-modal large model to the design of dialect cultural symbols, analyzes the resources of Nanning dialect, and puts forward the idea of cross-modal alignment and dynamic adaptation mechanism to realize the accurate extraction and creative design of dialect cultural symbols. With the help of the multi-modal large model, the recognition accuracy and emotional expression of dialect cultural symbols will be significantly improved, and the design scheme will be generated by the use of digital and intelligent technology, which can not only enrich the theoretical system of multi-modal intelligent design, but also provide a new path for regional cultural inheritance and design innovation, and effectively solve the industry pain point of the separation of cultural connotation and functional form in traditional cultural and creative design.
References
Mukherjee, A., & Ghosh, S. (2025). Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 1491-1501).
Han M., Zhu D., Wen X., Shu L., Yao Z.Research on Dialect Protection: Interaction Design of Chinese Dialects Based on BLSTM-CRF and FBM Theories.(2024) IEEE Access, 12, pp. 22059 - 22071
Song C.Dialect connectedness and tunneling: evidence from China.(2025) International Journal of Emerging Markets, 20 (2), pp. 678 - 700
Сапожникова, О. А. (2025). Діалекти Англії як джерело культурної ідентичності.
Feng Z.Research on the design strategy of integrating artificial intelligence into the visual transformation workshop of Sichuan and Chongqing dialects.(2025) Proceedings of 2025 International Conference on Artificial Intelligence and Smart Manufacturing, ICAISM 2025, pp. 906 - 910.
Cao Y., Li S., Liu Y., Yan Z., Dai Y., Yu P., Sun L.A Survey of AI-Generated Content (AIGC).(2025) ACM Computing Surveys, 57 (5).
Awais M., Naseer M., Khan S., Anwer R.M., Cholakkal H., Shah M., Yang M.-H., Khan F.S.Foundation Models Defining a New Era in Vision: A Survey and Outlook.(2025) IEEE Transactions on Pattern Analysis and Machine Intelligence, 47 (4)
Yang, L., Lin, Q., Qiu, J., He, J., & Li, Y. (2025, July). The Application of Dialect Voice in Game Character Design. In 2025 IEEE Gaming, Entertainment, and Media Conference (GEM) (pp. 1-6). IEEE.
Henryanto, Y. (2022). Design Translation Application from Indonesian to the Nyow Dialect (Pepadun) Based on Android. International Journal Software Engineering and Computer Science (IJSECS), 2(1), 18-25.
Han, M., Zhu, D., Wen, X., Shu, L., & Yao, Z. (2024). Research on dialect protection: interaction design of Chinese dialects based on BLSTM-CRF and FBM theories. IEEE Access, 12, 22059-22071.
Zhou, Y., An, S., Deng, H., Yin, D., Peng, C., Hsieh, C. J., ... & Peng, N. (2025). DialectGen: Benchmarking and Improving Dialect Robustness in Multimodal Generation. arxiv preprint arxiv:2510.14949.
Liao M., Guo S.Research on the Context Ambiguity Resolution Model of Cross-cultural Communication Based on Natural Language Processing.(2025) 2025 5th International Symposium on Computer Technology and Information Science, ISCTIS 2025, pp. 293 - 297.
Edalat A., Kamkar H., Mohammad A., Nojavan S., Aghamiri S., Fakhraie S.M., Yajam H., Mohammadsadegh.(2025) 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025.
Zhang, Y., He, Y., **a, Y., Wang, Y., Dong, X., & Yao, J. (2024). Exploring the representation of Chinese cultural symbols dissemination in the era of large language models. International Communication of Chinese Culture, 11(2), 215-237.
Zhao Y., Ding Y., Min X.Construction of a multimodal dialect corpus based on deep learning and digital twin technology: A case study on the Hangzhou dialect.(2025) Journal of Computational Methods in Sciences and Engineering, 25 (2), pp. 1448 - 1460.
Lizardo, O. (2016). Cultural symbols and cultural power. Qualitative Sociology, 39(2), 199-204.
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