Scaling Up LLM Reviews for Google Ads Content Moderation
Wei Qiao Tushar Dogra Otilia Stretcu Yu-Han Lyu Tiantian Fang Dongjin Kwon Chun-Ta Lu Enming Luo Yuan Wang Chih-Chun Chia Ariel Fuxman Fangzhou Wang Ranjay Krishna Mehmet Tek
Large language models (LLMs) are powerful tools for content moderation, but
their inference costs and latency make them prohibitive for casual use on large
datasets, such as the Google Ads repository. This study proposes a method for
scaling up LLM reviews for content moderation in Google Ads. First, we use
heuristics to select candidates via filtering and duplicate removal, and create
clusters of ads for which we select one representative ad per cluster. We then
use LLMs to review only the representative ads. Finally, we propagate the LLM
decisions for the representative ads back to their clusters. This method
reduces the number of reviews by more than 3 orders of magnitude while
achieving a 2x recall compared to a baseline non-LLM model. The success of this
approach is a strong function of the representations used in clustering and
label propagation; we found that cross-modal similarity representations yield
better results than uni-modal representations.