[fix] Update README.md with a typo

opencompass/configs/datasets/chinese_simpleqa/README.md

@@ -14,7 +14,7 @@ Please visit our [website](https://openstellarteam.github.io/ChineseSimpleQA/) o
 
 ## 💫 Instroduction
 
-* How to solve the generative hallucination of models has always been an unsolved problem in the field of artificial intelligence (AI). In order to measure the factual correctness of language models, OpenAI recently released and open-sourced a test set called SimpleQA. We have also been paying attention to the field of  factuality, which currently has problems such as outdated data, inaccurate evaluation, and incomplete coverage. For example, the knowledge evaluation sets widely used now are still CommonSenseQA, CMMLU, and C-Eval, which are multiple-choice question-based evaluation sets. **In order to further promote the research of the Chinese community on the factual correctness of models, we propose the Chinese SimpleQA**.  which consists of 3000 high-quality questions spanning 6 major topics, ranging from humanities to science and engineering. Specifically, the distinct main features of our proposed Chinese SimpleQA dataset are as follows:
+* How to solve the generative hallucination of models has always been an unsolved problem in the field of artificial intelligence (AI). In order to measure the factual correctness of language models, OpenAI recently released and open-sourced a test set called SimpleQA. We have also been paying attention to the field of factuality, which currently has problems such as outdated data, inaccurate evaluation, and incomplete coverage. For example, the knowledge evaluation sets widely used now are still CommonSenseQA, CMMLU, and C-Eval, which are multiple-choice question-based evaluation sets. **In order to further promote the research of the Chinese community on the factual correctness of models, we propose the Chinese SimpleQA**.  which consists of 3000 high-quality questions spanning 6 major topics, ranging from humanities to science and engineering. Specifically, the distinct main features of our proposed Chinese SimpleQA dataset are as follows:
   * 🀄**Chinese:** Our Chinese SimpleQA focuses on the Chinese language, which provides a comprehensive evaluation of the factuality abilities of existing LLMs in Chinese.
   * 🍀**Diverse:** Chinese SimpleQA covers 6 topics (i.e., “Chinese Culture”, “Humanities”, “Engineering, Technology, and Applied Sciences”, “Life, Art, and Culture”, “Society”, and “Natural Science”), and these topic includes 99 fine-grained subtopics in total, which demonstrates the diversity of our Chinese SimpleQA. 
   * ⚡**High-quality:** We conduct a comprehensive and rigorous quality control process to ensure the quality and accuracy of our Chinese SimpleQA.