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| INTRODUCTION_TEXT = """ | |
| <span style="font-size:16px; font-family: 'Times New Roman', serif;"> <b> Welcome to the ChineseSafe Leaderboard! | |
| On this leaderboard, we share the evaluation results of LLMs obtained by developing a brand new content moderation benchmark for Chinese. 🎉🎉🎉</b> | |
| </span> | |
| # Dataset | |
| <span style="font-size:16px; font-family: 'Times New Roman', serif"> | |
| To evaluate the conformity of large language models, we present ChineseSafe, a content moderation benchmark for Chinese (Mandarin). | |
| In this benchmark, we include 4 common types of safety issues: Crime, Ethic, Mental health, and their Variant/Homophonic words. | |
| In particular, the benchmark is constructed as a balanced dataset, containing safe and unsafe data collected from internet resources and public datasets [1,2,3]. | |
| We hope the evaluation can provide a reference for researchers and engineers to build safe LLMs in Chinese. <br> | |
| The leadboard is under construction and maintained by <a href="https://hongxin001.github.io/" target="_blank">Hongxin Wei's</a> research group at SUSTech. | |
| We will release the technical report in the near future. | |
| Comments, issues, contributions, and collaborations are all welcomed! | |
| Email: [email protected] | |
| </span> | |
| """ # noqa | |
| METRICS_TEXT = """ | |
| # Metrics | |
| <span style="font-size:16px; font-family: 'Times New Roman', serif"> | |
| We report the results with five metrics: overall accuracy, precision/recall for safe/unsafe content. | |
| In particular, the results are shown as <b>metric/std</b> format in the table, | |
| where <b>std</b> indicates the standard deviation of the results obtained from different random seeds. | |
| </span> | |
| """ # noqa | |
| EVALUTION_TEXT= """ | |
| # Evaluation | |
| <span style="font-size:16px; font-family: 'Times New Roman', serif"> | |
| We evaluate the models using two methods: multiple choice (perplexity) and generation. | |
| For perplexity, we select the label which is the lowest perplexity as the predicted results. | |
| For generation, we use the content generated by the model to make prediction. | |
| The following are the results of the evaluation. 👇👇👇 | |
| </span> <br><br> | |
| """ # noqa | |
| REFERENCE_TEXT = """ | |
| # References | |
| <span style="font-size:16px; font-family: 'Times New Roman', serif"> | |
| [1] Sun H, Zhang Z, Deng J, et al. Safety assessment of chinese large language models[J]. arXiv preprint arXiv:2304.10436, 2023. <br> | |
| [2] https://github.com/konsheng/Sensitive-lexicon <br> | |
| [3] https://www.cluebenchmarks.com/static/pclue.html <br> | |
| """ | |
| ACKNOWLEDGEMENTS_TEXT = """ | |
| # Acknowledgements | |
| <span style="font-size:16px; font-family: 'Times New Roman', serif"> | |
| This research is supported by "Data+AI" Data Intelligent Laboratory, | |
| a joint lab constructed by Deepexi and Department of Statistics and Data Science at SUSTech. | |
| We gratefully acknowledge the contributions of Prof. Bingyi Jing, Prof. Lili Yang, | |
| and Asst. Prof.Guanhua Chen for their support throughout this project. | |
| """ | |
| CONTACT_TEXT = """ | |
| # Contact | |
| <span style="font-size:16px; font-family: 'Times New Roman', serif"> | |
| The leadboard is under construction and maintained by <a href="https://hongxin001.github.io/" target="_blank">Hongxin Wei's</a> research group at SUSTech. | |
| We will release the technical report in the near future. | |
| Comments, issues, contributions, and collaborations are all welcomed! | |
| Email: [email protected] | |
| """ | |