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