Update src/display/about.py

Introduction of the new clcc dataset

src/display/about.py

@@ -40,7 +40,7 @@ We evaluate models on 7 key benchmarks using the <a href="https://github.com/Ele
 - <a href="https://arxiv.org/abs/2110.14168" target="_blank">  GSM8k </a> (5-shot) - diverse grade school math word problems to measure a model's ability to solve multi-step mathematical reasoning problems.
 - <a href="https://flageval.baai.ac.cn/#/taskIntro?t=zh_qa" target="_blank">  C-SEM </a>  (5-shot) - Semantic understanding is seen as a key cornerstone in the research and application of natural language processing. However, there is still a lack of publicly available benchmarks that approach from a linguistic perspective in the field of evaluating large Chinese language models.
 - <a href="https://arxiv.org/abs/2306.09212" target="_blank">  CMMLU </a>  (5-shot) - CMMLU is a comprehensive evaluation benchmark specifically designed to evaluate the knowledge and reasoning abilities of LLMs within the context of Chinese language and culture. CMMLU covers a wide range of subjects, comprising 67 topics that span from elementary to advanced professional levels.
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+- <a href="https://flageval.baai.ac.cn/#/taskIntro?t=zh_oqa">  CLCC </a> - CLCC is prepared by trained undergraduate or graduate students in different disciplines based on the FlagEval competency dimensions. 
 For all these evaluations, a higher score is a better score.
 We chose these benchmarks as they test a variety of reasoning and general knowledge across a wide variety of fields in 0-shot and few-shot settings.