This study addresses categories of harm surrounding Large Language Models (LLMs) in the field of artificial intelligence. It addresses five categories of harms addressed before, during, and after development of AI applications: pre-development, direct output, Misuse and Malicious Application, and downstream application. By underscoring the need to define risks of the current landscape to ensure accountability, transparency and navigating bias when adapting LLMs for practical applications. It proposes mitigation strategies and future directions for specific domains and a dynamic auditing system guiding responsible development and integration of LLMs in a standardized proposal.

翻译：本研究探讨了人工智能领域中围绕大型语言模型（LLM）的危害类别。它涵盖了人工智能应用开发前、开发中及开发后所涉及的五大危害类别：开发前阶段、直接输出、滥用与恶意应用，以及下游应用。通过强调在当前环境下定义风险的必要性，以确保在将LLM适配于实际应用时实现问责性、透明度并应对偏见。本研究针对特定领域提出了缓解策略与未来方向，并构建了一套动态审计系统，以标准化的提案指导LLM的责任开发与整合。