Deconstructing The Ethics Of Large Language Models From Long-standing Issues To New-emerging Dilemmas

Deng Chengyuan, Duan Yiqun, Jin Xin, Chang Heng, Tian Yijun, Liu Han, Zou Henry Peng, Jin Yiqiao, Xiao Yijia, Wang Yichen, Wu Shenghao, Xie Zongxing, Gao Kuofeng, He Sihong, Zhuang Jun, Cheng Lu, Wang Haohan. Arxiv 2024

[Paper]    
Ethics And Bias Language Modeling Merging Responsible AI Survey Paper

Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns, impacting the deployment of LLMs in everyday contexts. This paper provides a comprehensive survey of ethical challenges associated with LLMs, from longstanding issues such as copyright infringement, systematic bias, and data privacy, to emerging problems like truthfulness and social norms. We critically analyze existing research aimed at understanding, examining, and mitigating these ethical risks. Our survey underscores integrating ethical standards and societal values into the development of LLMs, thereby guiding the development of responsible and ethically aligned language models.

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