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As generative large model capabilities advance, safety concerns become morepronounced in their outputs. To ensure the sustainable growth of the AIecosystem, it's imperative to undertake a holistic evaluation and refinement ofassociated safety risks. This survey presents a framework for safety researchpertaining to large models, delineating the landscape of safety risks as wellas safety evaluation and improvement methods. We begin by introducing safetyissues of wide concern, then delve into safety evaluation methods for largemodels, encompassing preference-based testing, adversarial attack approaches,issues detection, and other advanced evaluation methods. Additionally, weexplore the strategies for enhancing large model safety from training todeployment, highlighting cutting-edge safety approaches for each stage inbuilding large models. Finally, we discuss the core challenges in advancingtowards more responsible AI, including the interpretability of safetymechanisms, ongoing safety issues, and robustness against malicious attacks.Through this survey, we aim to provide clear technical guidance for safetyresearchers and encourage further study on the safety of large models.

Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements