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Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey (2406.05392v2)

Published 8 Jun 2024 in cs.CL, cs.AI, cs.CY, and cs.LG

Abstract: LLMs have achieved unparalleled success across diverse LLMing 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 LLMs.

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Authors (18)
  1. Chengyuan Deng (18 papers)
  2. Yiqun Duan (34 papers)
  3. Xin Jin (285 papers)
  4. Heng Chang (32 papers)
  5. Yijun Tian (29 papers)
  6. Han Liu (340 papers)
  7. Henry Peng Zou (26 papers)
  8. Yiqiao Jin (27 papers)
  9. Yijia Xiao (19 papers)
  10. Yichen Wang (61 papers)
  11. Shenghao Wu (6 papers)
  12. Zongxing Xie (2 papers)
  13. Kuofeng Gao (23 papers)
  14. Sihong He (14 papers)
  15. Jun Zhuang (34 papers)
  16. Lu Cheng (73 papers)
  17. Haohan Wang (96 papers)
  18. Weimin Lyu (19 papers)
Citations (10)
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