Update README.md

README.md

@@ -495,14 +495,17 @@ license: apache-2.0
 
 ### Dataset Details
 
-The m-WildVision dataset is a multilingual multimodal LLM evaluation set. This dataset was created by translating the prompts from the originally English-only [WildVision (vision_bench_0617)](https://arxiv.org/abs/2406.11069) test dataset using Google Translate API v3 to 22 languages. 
-The original English-only prompts were created by Lu et al. (2024) and consist of 500 challenging user queries sourced from their WildVision-Arena platform. 
-The authors show that these can be used to perform automatic LLM judge evaluations, which exhibit a high correlation with WildVision-Arena rankings. 
+The m-WildVision dataset is a multilingual multimodal LLM evaluation set covering **23 languages**.  It was created by translating prompts from the original English-only [WildVision (vision_bench_0617)](https://arxiv.org/abs/2406.11069) test set. 
+The original prompts, developed by [Lu et al. (2024)](https://arxiv.org/abs/2406.11069) , consist of 500 challenging user queries sourced from the WildVision-Arena platform. 
+The authors demonstrated that these prompts enable automatic LLM judge evaluations, which strongly correlate with WildVision-Arena rankings. 
 
 ### Languages:
 
-This dataset covers 23 languages: Arabic (arb_Arab), Chinese (zho_Hans), Czech (ces_Latn), Dutch (nld_Latn), English (eng_Latn), French (fra_Latn), German (deu_Latn), Greek (ell_Grek), Hebrew (heb_Hebr), Hindi (hin_Deva), Indonesian (ind_Latn), Italian (ita_Latn), Japanese (jpn_Jpan), Korean (kor_Hang), Persian (fas_Arab), Polish (pol_Latn), Portuguese (por_Latn), Romanian (ron_Latn), Russian (rus_Cyrl), Spanish (spa_Latn), Turkish (tur_Latn), Ukrainian (ukr_Cyrl), and Vietnamese (vie_Latn).
+To ensure multilingual coverage, the non-English portion of the dataset was generated by translating the English subset into 22 additional languages using Google Translate API v3. The dataset includes a diverse range of language families (such as Latin-based languages like French, German, Arabic families like Persian and Arabic, and East Asian languages like Chinese, Korean, and Japanese) and scripts, ensuring a comprehensive evaluation of model generalizability and robustness.
 
+The languages included are: Arabic (arb_Arab), Chinese (zho_Hans), Czech (ces_Latn), Dutch (nld_Latn), English (eng_Latn), French (fra_Latn), German (deu_Latn), Greek (ell_Grek), Hebrew (heb_Hebr), Hindi (hin_Deva), Indonesian (ind_Latn), Italian (ita_Latn), Japanese (jpn_Jpan), Korean (kor_Hang), Persian (fas_Arab), Polish (pol_Latn), Portuguese (por_Latn), Romanian (ron_Latn), Russian (rus_Cyrl), Spanish (spa_Latn), Turkish (tur_Latn), Ukrainian (ukr_Cyrl), and Vietnamese (vie_Latn).
+
+By incorporating languages from different families and scripts, this benchmark enables a **comprehensive assessment of vision-language models**, particularly their ability to generalize across diverse languages.
 
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