Add paper and code links

README.md

@@ -1,5 +1,20 @@
 ---
+language:
+- en
+- zh
+- hi
+- es
+- ar
+- fr
+- pt
+- ru
+- ja
+- ko
 license: cc-by-nc-sa-4.0
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-to-image
 dataset_info:
   features:
   - name: data_id
@@ -34,25 +49,13 @@ configs:
     path: data/content_generation-*
   - split: text_rendering
     path: data/text_rendering-*
-task_categories:
-- text-to-image
-language:
-- en
-- zh
-- hi
-- es
-- ar
-- fr
-- pt
-- ru
-- ja
-- ko
-size_categories:
-- 10K<n<100K
 ---
 
 # LingT2I (TRIG-Multilingual)
 
+**Paper:** [On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation](https://huggingface.co/papers/2608.11002)  
+**Code:** [https://github.com/RISys-Lab/LingT2I](https://github.com/RISys-Lab/LingT2I)
+
 LingT2I (TRIG-Multilingual) is a multilingual image-generation benchmark with two subsets:
 
 - `content_generation`: multilingual prompts for evaluating generated image content across dimensions.
@@ -102,4 +105,4 @@ print(sample_tr["condition_image"])  # PIL.Image.Image for text placement
 
 - `content_generation` contains 30,000 prompts across 10 languages and 10 dimensions.
 - `text_rendering` contains 3,000 prompts across 10 languages.
-- `raw/coarse_mask.zip` is kept for traceability; the same placement images are embedded in the `condition_image` column of the `text_rendering` split.
+- `raw/coarse_mask.zip` is kept for traceability; the same placement images are embedded in the `condition_image` column of the `text_rendering` split.