Researchers and developers increasingly rely on toxicity scoring to moderate\ngenerative language model outputs, in settings such as customer service,\ninformation retrieval, and content generation. However, toxicity scoring may\nrender pertinent information inaccessible, rigidify or "value-lock" cultural\nnorms, and prevent language reclamation processes, particularly for\nmarginalized people. In this work, we extend the concept of algorithmic\nrecourse to generative language models: we provide users a novel mech…