  Extensive efforts in automated approaches for content moderation have been
focused on developing models to identify toxic, offensive, and hateful content
with the aim of lightening the load for moderators. Yet, it remains uncertain
whether improvements on those tasks have truly addressed moderators' needs in
accomplishing their work. In this paper, we surface gaps between past research
efforts that have aimed to provide automation for aspects of content moderation
and the needs of volunteer content moderators, regarding identifying violations
of various moderation rules. To do so, we conduct a model review on Hugging
Face to reveal the availability of models to cover various moderation rules and
guidelines from three exemplar forums. We further put state-of-the-art LLMs to
the test, evaluating how well these models perform in flagging violations of
platform rules from one particular forum. Finally, we conduct a user survey
study with volunteer moderators to gain insight into their perspectives on
useful moderation models. Overall, we observe a non-trivial gap, as missing
developed models and LLMs exhibit moderate to low performance on a significant
portion of the rules. Moderators' reports provide guides for future work on
developing moderation assistant models.
