Update README.md

README.md

@@ -47,6 +47,7 @@ configs:
 VisualSimpleQA is a multimodal fact-seeking benchmark with two key features. First, it enables streamlined and decoupled evaluation of LVLMs in visual and linguistic modalities. Second, it incorporates well-defined difficulty criteria to guide human annotation and facilitates the extraction of a challenging subset, VisualSimpleQA-hard.
 Experiments on 15 LVLMs show that even state-of-the-art models such as GPT-4o achieve merely 60%+ correctness in multimodal fact-seeking QA on VisualSimpleQA and 30%+ on VisualSimpleQA-hard.
 Furthermore, the decoupled evaluation based on this benchmark across different models highlights substantial opportunities for improvement in both visual and linguistic modules.
+The dataset reviewer above illustrates 129 samples from VisualSimpleQA-hard.
 
 **Data Example:**
 ```
@@ -65,6 +66,13 @@ Furthermore, the decoupled evaluation based on this benchmark across different m
    'cropped_image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=164x196 at 0x7FE82C329550>}
 ```
 
+## File Structure
+
+`data/`
+This directory contains all 500 samples of VisualSimpleQA stored in parquet files.
+
+`hard_data/`
+This directory contains 129 VisualSimpleQA-hard samples stored in a parquet file. These samples are selected based on well-defined criteria to ensure they represent more challenging cases from VisualSimpleQA.
 
 ## Disclaimer