Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence
This work addresses the need to understand internal model changes during detoxification for researchers and practitioners, though it is incremental as it builds on existing methods.
The study investigated how different detoxification techniques affect language models' reliance on prompts, finding that counter-narrative fine-tuning and reinforcement learning-based methods show distinct prompt dependence patterns despite achieving similar detoxification results.
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.