The content of the paper discusses the safety implications of customizing large language models (LLMs) and the potential for biased or harmful outputs. The researchers introduce a new method called ForgetFilter, which aims to filter out unsafe examples during the finetuning process to mitigate these risks. They also explore the impact of different parameters, such as forgetting rates and the size of safe examples, on the effectiveness of ForgetFilter. Additionally, the research delves into the long-term safety of LLMs and the ethical considerations of biased or harmful outputs. The study contributes to the ongoing dialogue on AI ethics and safety and offers a valuable solution for responsible development and deployment of LLMs. The paper addresses the multifaceted safety challenges in LLMs and prompts future investigations into the factors influencing LLM forgetting behaviors. ForgetFilter is identified as a critical step toward responsible AI development and deployment. The research team also emphasizes the importance of mitigating risks through advanced safety measures and ethical consciousness. Finally, the paper encourages further exploration of the factors influencing LLM forgetting behaviors and offers valuable insights for the AI community.