  As generative large model capabilities advance, safety concerns become more
pronounced in their outputs. To ensure the sustainable growth of the AI
ecosystem, it's imperative to undertake a holistic evaluation and refinement of
associated safety risks. This survey presents a framework for safety research
pertaining to large models, delineating the landscape of safety risks as well
as safety evaluation and improvement methods. We begin by introducing safety
issues of wide concern, then delve into safety evaluation methods for large
models, encompassing preference-based testing, adversarial attack approaches,
issues detection, and other advanced evaluation methods. Additionally, we
explore the strategies for enhancing large model safety from training to
deployment, highlighting cutting-edge safety approaches for each stage in
building large models. Finally, we discuss the core challenges in advancing
towards more responsible AI, including the interpretability of safety
mechanisms, ongoing safety issues, and robustness against malicious attacks.
Through this survey, we aim to provide clear technical guidance for safety
researchers and encourage further study on the safety of large models.
