  Large pre-trained neural language models have supported the effectiveness of
many NLP tasks, yet are still prone to generating toxic language hindering the
safety of their use. Using empathetic data, we improve over recent work on
controllable text generation that aims to reduce the toxicity of generated
text. We find we are able to dramatically reduce the size of fine-tuning data
to 7.5-30k samples while at the same time making significant improvements over
state-of-the-art toxicity mitigation of up to 3.4% absolute reduction (26%
relative) from the original work on 2.3m samples, by strategically sampling
data based on empathy scores. We observe that the degree of improvement is
subject to specific communication components of empathy. In particular, the
cognitive components of empathy significantly beat the original dataset in
almost all experiments, while emotional empathy was tied to less improvement
and even underperforming random samples of the original data. This is a
particularly implicative insight for NLP work concerning empathy as until
recently the research and resources built for it have exclusively considered
empathy as an emotional concept.
