Scaling Up LLM Reviews for Google Ads Content Moderation
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929252955324416 |
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| author | Qiao, Wei Dogra, Tushar Stretcu, Otilia Lyu, Yu-Han Fang, Tiantian Kwon, Dongjin Lu, Chun-Ta Luo, Enming Wang, Yuan Chia, Chih-Chun Fuxman, Ariel Wang, Fangzhou Krishna, Ranjay Tek, Mehmet |
| author_facet | Qiao, Wei Dogra, Tushar Stretcu, Otilia Lyu, Yu-Han Fang, Tiantian Kwon, Dongjin Lu, Chun-Ta Luo, Enming Wang, Yuan Chia, Chih-Chun Fuxman, Ariel Wang, Fangzhou Krishna, Ranjay Tek, Mehmet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scaling Up LLM Reviews for Google Ads Content Moderation Qiao, Wei Dogra, Tushar Stretcu, Otilia Lyu, Yu-Han Fang, Tiantian Kwon, Dongjin Lu, Chun-Ta Luo, Enming Wang, Yuan Chia, Chih-Chun Fuxman, Ariel Wang, Fangzhou Krishna, Ranjay Tek, Mehmet Information Retrieval Computation and Language Machine Learning 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. |
| title | Scaling Up LLM Reviews for Google Ads Content Moderation |
| topic | Information Retrieval Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.14590 |