Recent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings. In parallel, these improvements have prompted a heated discourse around the risks of societal harms they introduce, whether inadvertent or malicious. Several studies have explored these harms and called for their mitigation via development of safer, fairer models. Going beyond enumerating the risks of harms, this work provides a survey of practical methods for addressing potential threats and societal harms from language generation models. We draw on several prior works' taxonomies of language model risks to present a structured overview of strategies for detecting and ameliorating different kinds of risks/harms of language generators. Bridging diverse strands of research, this survey aims to serve as a practical guide for both LM researchers and practitioners, with explanations of different mitigation strategies' motivations, their limitations, and open problems for future research.

翻译：近期，大型语言模型生成类人文本能力的提升，导致其在面向用户的场景中得到更广泛应用。与此同时，这些进步引发了关于它们所引入的（无论无意或恶意）社会危害风险的激烈讨论。多项研究已探讨了这些危害，并通过开发更安全、更公平的模型呼吁对其加以缓解。在超越单纯列举危害风险的基础上，本文提供了针对语言生成模型潜在威胁与社会危害的实用方法综述。我们借鉴先前多项研究的语言模型风险分类体系，系统梳理了检测与缓解各类语言生成器风险/危害的策略。通过整合不同研究脉络，本综述旨在为语言模型研究人员与实践者提供实用指南，详细阐释各类缓解策略的动机、局限性及未来研究的开放性问题。