  Counterspeech, defined as a response to mitigate online hate speech, is
increasingly used as a non-censorial solution. Addressing hate speech
effectively involves dispelling the stereotypes, prejudices, and biases often
subtly implied in brief, single-sentence statements or abuses. These implicit
expressions challenge language models, especially in seq2seq tasks, as model
performance typically excels with longer contexts. Our study introduces CoARL,
a novel framework enhancing counterspeech generation by modeling the pragmatic
implications underlying social biases in hateful statements. CoARL's first two
phases involve sequential multi-instruction tuning, teaching the model to
understand intents, reactions, and harms of offensive statements, and then
learning task-specific low-rank adapter weights for generating
intent-conditioned counterspeech. The final phase uses reinforcement learning
to fine-tune outputs for effectiveness and non-toxicity. CoARL outperforms
existing benchmarks in intent-conditioned counterspeech generation, showing an
average improvement of 3 points in intent-conformity and 4 points in
argument-quality metrics. Extensive human evaluation supports CoARL's efficacy
in generating superior and more context-appropriate responses compared to
existing systems, including prominent LLMs like ChatGPT.
