Update README.md

README.md

@@ -18,15 +18,15 @@ BenchLMM is a benchmarking dataset focusing on the cross-style visual capability
 ### Dataset Description
 
 - **Curated by:** Rizhao Cai, Zirui Song, Dayan Guan, Zhenhao Chen, Xing Luo, Chenyu Yi, and Alex Kot.
-- **Funded by [optional]:** Supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute.
-- **Shared by [optional]:** AIFEG.
+- **Funded by :** Supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute.
+- **Shared by :** AIFEG.
 - **Language(s) (NLP):** English.
 - **License:** Apache-2.0.
 
-### Dataset Sources [optional]
+### Dataset Sources 
 
 - **Repository:** [GitHub - AIFEG/BenchLMM](https://github.com/AIFEG/BenchLMM)
-- **Paper [optional]:** Cai, R., Song, Z., Guan, D., et al. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv:2312.02896.
+- **Paper :** Cai, R., Song, Z., Guan, D., et al. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv:2312.02896.
 
 ## Uses
 
@@ -58,7 +58,7 @@ The dataset consists of various visual questions and corresponding answers, stru
 
 Users should consider the specific visual contexts and question types included in the dataset when interpreting model performance.
 
-## Citation [optional]
+## Citation 
 
 **BibTeX:**
 @misc{cai2023benchlmm,