@inproceedings{vasilev-etal-2025-ruscode,
title = "{R}us{C}ode: {R}ussian Cultural Code Benchmark for Text-to-Image Generation",
author = "Vasilev, Viacheslav and
Agafonova, Julia and
Gerasimenko, Nikolai and
Kapitanov, Alexander and
Mikhailova, Polina and
Mironova, Evelina and
Dimitrov, Denis",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.425/",
doi = "10.18653/v1/2025.findings-naacl.425",
pages = "7641--7657",
ISBN = "979-8-89176-195-7",
abstract = "Text-to-image generation models have gained popularity among users around the world. However, many of these models exhibit a strong bias toward English-speaking cultures, ignoring or misrepresenting the unique characteristics of other language groups, countries, and nationalities. The lack of cultural awareness can reduce the generation quality and lead to undesirable consequences such as unintentional insult, and the spread of prejudice. In contrast to the field of natural language processing, cultural awareness in computer vision has not been explored as extensively. In this paper, we strive to reduce this gap. We propose a RusCode benchmark for evaluating the quality of text-to-image generation containing elements of the Russian cultural code. To do this, we form a list of 19 categories that best represent the features of Russian visual culture. Our final dataset consists of 1250 text prompts in Russian and their translations into English. The prompts cover a wide range of topics, including complex concepts from art, popular culture, folk traditions, famous people{'}s names, natural objects, scientific achievements, etc. We present the results of a human evaluation of the side-by-side comparison of Russian visual concepts representations using popular generative models."
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<abstract>Text-to-image generation models have gained popularity among users around the world. However, many of these models exhibit a strong bias toward English-speaking cultures, ignoring or misrepresenting the unique characteristics of other language groups, countries, and nationalities. The lack of cultural awareness can reduce the generation quality and lead to undesirable consequences such as unintentional insult, and the spread of prejudice. In contrast to the field of natural language processing, cultural awareness in computer vision has not been explored as extensively. In this paper, we strive to reduce this gap. We propose a RusCode benchmark for evaluating the quality of text-to-image generation containing elements of the Russian cultural code. To do this, we form a list of 19 categories that best represent the features of Russian visual culture. Our final dataset consists of 1250 text prompts in Russian and their translations into English. The prompts cover a wide range of topics, including complex concepts from art, popular culture, folk traditions, famous people’s names, natural objects, scientific achievements, etc. We present the results of a human evaluation of the side-by-side comparison of Russian visual concepts representations using popular generative models.</abstract>
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%0 Conference Proceedings
%T RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation
%A Vasilev, Viacheslav
%A Agafonova, Julia
%A Gerasimenko, Nikolai
%A Kapitanov, Alexander
%A Mikhailova, Polina
%A Mironova, Evelina
%A Dimitrov, Denis
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F vasilev-etal-2025-ruscode
%X Text-to-image generation models have gained popularity among users around the world. However, many of these models exhibit a strong bias toward English-speaking cultures, ignoring or misrepresenting the unique characteristics of other language groups, countries, and nationalities. The lack of cultural awareness can reduce the generation quality and lead to undesirable consequences such as unintentional insult, and the spread of prejudice. In contrast to the field of natural language processing, cultural awareness in computer vision has not been explored as extensively. In this paper, we strive to reduce this gap. We propose a RusCode benchmark for evaluating the quality of text-to-image generation containing elements of the Russian cultural code. To do this, we form a list of 19 categories that best represent the features of Russian visual culture. Our final dataset consists of 1250 text prompts in Russian and their translations into English. The prompts cover a wide range of topics, including complex concepts from art, popular culture, folk traditions, famous people’s names, natural objects, scientific achievements, etc. We present the results of a human evaluation of the side-by-side comparison of Russian visual concepts representations using popular generative models.
%R 10.18653/v1/2025.findings-naacl.425
%U https://aclanthology.org/2025.findings-naacl.425/
%U https://doi.org/10.18653/v1/2025.findings-naacl.425
%P 7641-7657
Markdown (Informal)
[RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation](https://aclanthology.org/2025.findings-naacl.425/) (Vasilev et al., Findings 2025)
ACL
- Viacheslav Vasilev, Julia Agafonova, Nikolai Gerasimenko, Alexander Kapitanov, Polina Mikhailova, Evelina Mironova, and Denis Dimitrov. 2025. RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 7641–7657, Albuquerque, New Mexico. Association for Computational Linguistics.