Can Language Model Moderators Improve the Health of Online Discourse?
This addresses the challenge of scaling moderation in online communities to improve discourse health, though it is incremental in evaluating existing methods rather than introducing new ones.
The paper tackled the problem of evaluating language models as conversational moderators for online discourse, establishing a systematic evaluation framework and finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to increase user respect and cooperation.
Conversational moderation of online communities is crucial to maintaining civility for a constructive environment, but it is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier to aid human moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness grounded on moderation literature and establish design criteria for conducting realistic yet safe evaluation. We then propose a comprehensive evaluation framework to assess models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of language models as conversational moderators, finding that appropriately prompted models that incorporate insights from social science can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.