GTA: Gated Toxicity Avoidance for LM Performance Preservation
Heegyu Kim Hyunsouk Cho
Caution: This paper includes offensive words that could potentially cause
unpleasantness. The fast-paced evolution of generative language models such as
GPT-4 has demonstrated outstanding results in various NLP generation tasks.
However, due to the potential generation of offensive words related to race or
gender, various Controllable Text Generation (CTG) methods have been proposed
to mitigate the occurrence of harmful words. However, existing CTG methods not
only reduce toxicity but also negatively impact several aspects of the language
model's generation performance, including topic consistency, grammar, and
perplexity. This paper explores the limitations of previous methods and
introduces a novel solution in the form of a simple Gated Toxicity Avoidance
(GTA) that can be applied to any CTG method. We also evaluate the effectiveness
of the proposed GTA by comparing it with state-of-the-art CTG methods across
various datasets. Our findings reveal that gated toxicity avoidance efficiently
achieves comparable levels of toxicity reduction to the original CTG methods
while preserving the generation performance of the language model.