Recent breakthroughs in natural language processing (NLP) have permitted the synthesis and comprehension of coherent text in an open-ended way, therefore translating the theoretical algorithms into practical applications. The large language-model (LLM) has significantly impacted businesses such as report summarization softwares and copywriters. Observations indicate, however, that LLMs may exhibit social prejudice and toxicity, posing ethical and societal dangers of consequences resulting from irresponsibility. Large-scale benchmarks for accountable LLMs should consequently be developed. Although several empirical investigations reveal the existence of a few ethical difficulties in advanced LLMs, there is no systematic examination and user study of the ethics of current LLMs use. To further educate future efforts on constructing ethical LLMs responsibly, we perform a qualitative research method on OpenAI's ChatGPT to better understand the practical features of ethical dangers in recent LLMs. We analyze ChatGPT comprehensively from four perspectives: 1) \textit{Bias} 2) \textit{Reliability} 3) \textit{Robustness} 4) \textit{Toxicity}. In accordance with our stated viewpoints, we empirically benchmark ChatGPT on multiple sample datasets. We find that a significant number of ethical risks cannot be addressed by existing benchmarks, and hence illustrate them via additional case studies. In addition, we examine the implications of our findings on the AI ethics of ChatGPT, as well as future problems and practical design considerations for LLMs. We believe that our findings may give light on future efforts to determine and mitigate the ethical hazards posed by machines in LLM applications.

翻译：自然语言处理领域的最新突破使得以开放方式合成和理解连贯文本成为可能，从而将理论算法转化为实际应用。大型语言模型深刻影响了诸如报告摘要软件和文案撰写等商业领域。然而，观察表明，大型语言模型可能表现出社会偏见和毒性，带来因不负责任行为导致的伦理与社会风险。因此，需要开发负责任大型语言模型的大规模基准测试。尽管多项实证研究表明高级大型语言模型存在若干伦理难题，但当前尚无对大型语言模型伦理使用进行系统性检验和用户研究。为更负责任地指导未来构建伦理大型语言模型的工作，我们对OpenAI的ChatGPT采用定性研究方法，以深入理解当前大型语言模型中伦理风险的实际特征。我们从四个维度全面分析ChatGPT：1）偏见，2）可靠性，3）鲁棒性，4）毒性。依据既定视角，我们在多个示例数据集上对ChatGPT进行实证基准测试。我们发现，众多伦理风险无法通过现有基准测试解决，因此通过额外案例研究加以阐释。此外，我们探讨了研究结果对ChatGPT人工智能伦理的影响，以及大型语言模型面临的未来挑战和实际设计考量。我们相信，研究结论将为未来识别并缓解大型语言模型应用中机器引发的伦理风险提供启示。