Outcome-Constrained Large Language Models for Countering Hate Speech
Lingzi Hong Pengcheng Luo Eduardo Blanco Xiaoying Song
Counterspeech that challenges or responds to hate speech has been seen as an
alternative to mitigate the negative impact of hate speech and foster
productive online communications. Research endeavors have been directed to
using language models for the automatic generation of counterspeech to assist
efforts in combating online hate. Existing research focuses on the generation
of counterspeech with certain linguistic attributes, such as being polite,
informative, and intent-driven. However, it remains unclear what impact the
counterspeech might have in an online environment. We first explore methods
that utilize large language models (LLM) to generate counterspeech constrained
by potential conversation outcomes. We build two conversation outcome
classifiers that predict the incivility level and the hater reentry behavior
following replies to hate with Reddit data, then propose four methods to
incorporate the desired outcomes, i.e., low conversation incivility and
non-hateful hater reentry, into the text generation process, including Prompt
with Instructions, Prompt and Select, LLM finetune, and LLM transformer
reinforcement learning (TRL). Evaluation results show effective strategies to
generate outcome-constrained counterspeech and the linguistic characteristics
of texts generated by different methods.