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

• URL: http://arxiv.org/abs/2406.05392v2
• Date: Mon, 21 Oct 2024 07:08:11 GMT
• Title: Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
• Authors: Chengyuan Deng, Yiqun Duan, Xin Jin, Heng Chang, Yijun Tian, Han Liu, Yichen Wang, Kuofeng Gao, Henry Peng Zou, Yiqiao Jin, Yijia Xiao, Shenghao Wu, Zongxing Xie, Weimin Lyu, Sihong He, Lu Cheng, Haohan Wang, Jun Zhuang,
• Abstract summary: Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. This paper provides a comprehensive survey of ethical challenges associated with LLMs, from longstanding issues such as copyright infringement to emerging problems like truthfulness and social norms.
• Score: 27.689403365365685
• License:
• Abstract: 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.

Related papers

• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models [53.316174782223115]
  This whitepaper offers an overview of the ethical considerations surrounding research into or with large language models (LLMs) As LLMs become more integrated into widely used applications, their societal impact increases, bringing important ethical questions to the forefront.
  arXiv  Detail & Related papers  (2024-10-17T18:36:02Z)
• A Survey on the Honesty of Large Language Models [115.8458596738659]
  Honesty is a fundamental principle for aligning large language models (LLMs) with human values. Despite promising, current LLMs still exhibit significant dishonest behaviors.
  arXiv  Detail & Related papers  (2024-09-27T14:34:54Z)
• Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions [1.1970409518725493]
  The article highlights the application areas that could have a positive impact on society along with the ethical considerations. It includes responsible development considerations, algorithmic improvements, ethical challenges, and societal implications.
  arXiv  Detail & Related papers  (2024-09-25T14:36:30Z)
• Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey [0.0]
  Large Language Models (LLMs) have transformed Natural Language Processing (NLP) with their outstanding abilities in text generation, summarization, and classification. Their widespread adoption introduces numerous challenges, including issues related to academic integrity, copyright, environmental impacts, and ethical considerations such as data bias, fairness, and privacy. This paper offers a comprehensive survey of the literature on these subjects, systematically gathered and synthesized from Google Scholar.
  arXiv  Detail & Related papers  (2024-08-01T21:21:18Z)
• MoralBench: Moral Evaluation of LLMs [34.43699121838648]
  This paper introduces a novel benchmark designed to measure and compare the moral reasoning capabilities of large language models (LLMs) We present the first comprehensive dataset specifically curated to probe the moral dimensions of LLM outputs. Our methodology involves a multi-faceted approach, combining quantitative analysis with qualitative insights from ethics scholars to ensure a thorough evaluation of model performance.
  arXiv  Detail & Related papers  (2024-06-06T18:15:01Z)
• Navigating LLM Ethics: Advancements, Challenges, and Future Directions [5.023563968303034]
  This study addresses ethical issues surrounding Large Language Models (LLMs) within the field of artificial intelligence. It explores the common ethical challenges posed by both LLMs and other AI systems. It highlights challenges such as hallucination, verifiable accountability, and decoding censorship complexity.
  arXiv  Detail & Related papers  (2024-05-14T15:03:05Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Unpacking the Ethical Value Alignment in Big Models [46.560886177083084]
  This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models. We introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method.
  arXiv  Detail & Related papers  (2023-10-26T16:45:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.