Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910657601863680 |
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| author | Deng, Chengyuan Duan, Yiqun Jin, Xin Chang, Heng Tian, Yijun Liu, Han Wang, Yichen Gao, Kuofeng Zou, Henry Peng Jin, Yiqiao Xiao, Yijia Wu, Shenghao Xie, Zongxing Lyu, Weimin He, Sihong Cheng, Lu Wang, Haohan Zhuang, Jun |
| author_facet | Deng, Chengyuan Duan, Yiqun Jin, Xin Chang, Heng Tian, Yijun Liu, Han Wang, Yichen Gao, Kuofeng Zou, Henry Peng Jin, Yiqiao Xiao, Yijia Wu, Shenghao Xie, Zongxing Lyu, Weimin He, Sihong Cheng, Lu Wang, Haohan Zhuang, Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05392 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey Deng, Chengyuan Duan, Yiqun Jin, Xin Chang, Heng Tian, Yijun Liu, Han Wang, Yichen Gao, Kuofeng Zou, Henry Peng Jin, Yiqiao Xiao, Yijia Wu, Shenghao Xie, Zongxing Lyu, Weimin He, Sihong Cheng, Lu Wang, Haohan Zhuang, Jun Computation and Language Artificial Intelligence Computers and Society Machine Learning 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. |
| title | Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey |
| topic | Computation and Language Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2406.05392 |