Let the Models Respond: Interpreting Language Model Detoxification
  Through the Lens of Prompt Dependence
Daniel Scalena Gabriele Sarti Malvina Nissim Elisabetta Fersini
Due to language models' propensity to generate toxic or hateful responses,
several techniques were developed to align model generations with users'
preferences. Despite the effectiveness of such methods in improving the safety
of model interactions, their impact on models' internal processes is still
poorly understood. In this work, we apply popular detoxification approaches to
several language models and quantify their impact on the resulting models'
prompt dependence using feature attribution methods. We evaluate the
effectiveness of counter-narrative fine-tuning and compare it with
reinforcement learning-driven detoxification, observing differences in prompt
reliance between the two methods despite their similar detoxification
performances.