This paper aims to help structure the risk landscape associated with
large-scale Language Models (LMs). In order to foster advances in responsible
innovation, an in-depth understanding of the potential risks posed by these
models is needed. A wide range of established and anticipated risks are
analysed in detail, drawing on multidisciplinary expertise and literature from
computer science, linguistics, and social sciences.
We outline six specific risk areas: I. Discrimination, Exclusion and
Toxicity, II. Information Hazards, III. Misinformation Harms, V. Malicious
Uses, V. Human-Computer Interaction Harms, VI. Automation, Access, and
Environmental Harms. The first area concerns the perpetuation of stereotypes,
unfair discrimination, exclusionary norms, toxic language, and lower
performance by social group for LMs. The second focuses on risks from private
data leaks or LMs correctly inferring sensitive information. The third
addresses risks arising from poor, false or misleading information including in
sensitive domains, and knock-on risks such as the erosion of trust in shared
information. The fourth considers risks from actors who try to use LMs to cause
harm. The fifth focuses on risks specific to LLMs used to underpin
conversational agents that interact with human users, including unsafe use,
manipulation or deception. The sixth discusses the risk of environmental harm,
job automation, and other challenges that may have a disparate effect on
different social groups or communities.
In total, we review 21 risks in-depth. We discuss the points of origin of
different risks and point to potential mitigation approaches. Lastly, we
discuss organisational responsibilities in implementing mitigations, and the
role of collaboration and participation. We highlight directions for further
research, particularly on expanding the toolkit for assessing and evaluating
the outlined risks in LMs.

本文旨在帮助构建与大规模语言模型相关的风险场景。我们总结了 LMs 可能面临的各种风险，如歧视、错误信息、自动化等，并提出了应对方法和未来研究方向。