Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF
<|reference_start|>Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF: 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.<|reference_end|>
@article{hengle2024intent-conditioned,
    title={Intent-conditioned and Non-toxic Counterspeech Generation using
  Multi-Task Instruction Tuning with RLAIF},
    author={Amey Hengle, Aswini Kumar, Sahajpreet Singh, Anil Bandhakavi, Md Shad
  Akhtar, Tanmoy Chakroborty},
    journal={arXiv preprint arXiv:2403.10088},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10088},
    primaryClass={cs.CL cs.AI}
}