  Considerable effort has been dedicated to mitigating toxicity, but existing
methods often require drastic modifications to model parameters or the use of
computationally intensive auxiliary models. Furthermore, previous approaches
have often neglected the crucial factor of language's evolving nature over
time. In this work, we present a comprehensive perspective on toxicity
mitigation that takes into account its changing nature. We introduce
Goodtriever, a flexible methodology that matches the current state-of-the-art
toxicity mitigation while achieving 43% relative latency reduction during
inference and being more computationally efficient. By incorporating a
retrieval-based approach at decoding time, Goodtriever enables
toxicity-controlled text generation. Our research advocates for an increased
focus on adaptable mitigation techniques, which better reflect the data drift
models face when deployed in the wild. Code and data are available at
https://github.com/for-ai/goodtriever.
