Add paper and code links

This PR adds links to the paper and the GitHub repository in the dataset card, making it easier to reference the associated research and access the implementation.

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.