The paper "Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas" by Chengyuan Deng et al. provides a comprehensive survey of ethical challenges associated with Large Language Models (LLMs). The authors categorize these challenges into two main categories: longstanding issues and new-emerging problems. Longstanding issues include data privacy, copyright, and fairness, while new-emerging problems focus on truthfulness and social norms. The paper critically analyzes existing research aimed at understanding, examining, and mitigating these ethical risks, emphasizing the importance of integrating ethical standards and societal values into the development of LLMs. The introduction highlights the rapid advancements in LLMs and the associated ethical concerns, particularly in areas such as privacy, copyright, robustness, bias, and potential misuse. The paper outlines the need for responsible AI systems that maximize benefits and minimize harm, serving the public good ethically and effectively. The first section delves into enduring ethical dilemmas, including data privacy, copyright, and fairness. For data privacy, the paper discusses issues like memorization and privacy attacks, and introduces differential privacy as a primary scheme to address these concerns. It also explores pre-LLM and LLM-era techniques for privacy preservation, such as DP training, fine-tuning, and inference. For copyright, the paper examines backdoor and watermarking techniques to protect model ownership and intellectual property. Fairness issues are addressed through various mitigation strategies, including in-training, intra-processing, and post-processing methods. The second section focuses on new-emerging ethical issues, particularly truthfulness and social norms. Truthfulness is a critical concern due to issues like hallucination and sycophancy, which compromise the reliability and ethical deployment of LLMs. The paper discusses the underlying causes of hallucinations, including data quality, model architecture, and algorithmic limitations, and presents mitigation strategies such as data-centric and model-centric approaches. Overall, the paper aims to provide a systematic summary and categorization of existing ethical issues, propose new taxonomy, and discuss future research directions to guide the development of more responsible and ethically aligned LLMs.The paper "Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas" by Chengyuan Deng et al. provides a comprehensive survey of ethical challenges associated with Large Language Models (LLMs). The authors categorize these challenges into two main categories: longstanding issues and new-emerging problems. Longstanding issues include data privacy, copyright, and fairness, while new-emerging problems focus on truthfulness and social norms. The paper critically analyzes existing research aimed at understanding, examining, and mitigating these ethical risks, emphasizing the importance of integrating ethical standards and societal values into the development of LLMs. The introduction highlights the rapid advancements in LLMs and the associated ethical concerns, particularly in areas such as privacy, copyright, robustness, bias, and potential misuse. The paper outlines the need for responsible AI systems that maximize benefits and minimize harm, serving the public good ethically and effectively. The first section delves into enduring ethical dilemmas, including data privacy, copyright, and fairness. For data privacy, the paper discusses issues like memorization and privacy attacks, and introduces differential privacy as a primary scheme to address these concerns. It also explores pre-LLM and LLM-era techniques for privacy preservation, such as DP training, fine-tuning, and inference. For copyright, the paper examines backdoor and watermarking techniques to protect model ownership and intellectual property. Fairness issues are addressed through various mitigation strategies, including in-training, intra-processing, and post-processing methods. The second section focuses on new-emerging ethical issues, particularly truthfulness and social norms. Truthfulness is a critical concern due to issues like hallucination and sycophancy, which compromise the reliability and ethical deployment of LLMs. The paper discusses the underlying causes of hallucinations, including data quality, model architecture, and algorithmic limitations, and presents mitigation strategies such as data-centric and model-centric approaches. Overall, the paper aims to provide a systematic summary and categorization of existing ethical issues, propose new taxonomy, and discuss future research directions to guide the development of more responsible and ethically aligned LLMs.