105 citations · 186 across the 11 of their papers we have counts for
5 papers · 1 filter
Zero-Shot Image Moderation in Google Ads with LLM-Assisted Textual Descriptions and Cross-modal Co-embeddings
Enming Luo, Wei Qiao, Katie Warren +7
We present a scalable and agile approach for ads image content moderation at Google, addressing the challenges of moderating massive volumes of ads with diverse content and evolvin…
A Implies B: Circuit Analysis in LLMs for Propositional Logical Reasoning
Guan Zhe Hong, Nishanth Dikkala, Enming Luo +3
Due to the size and complexity of modern large language models (LLMs), it has proven challenging to uncover the underlying mechanisms that models use to solve reasoning problems. F…
Why Fine-grained Labels in Pretraining Benefit Generalization?
Guan Zhe Hong, Yin Cui, Ariel Fuxman +2
Recent studies show that pretraining a deep neural network with fine-grained labeled data, followed by fine-tuning on coarse-labeled data for downstream tasks, often yields better…
Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use
Imad Eddine Toubal, Aditya Avinash, Neil Gordon Alldrin +10
From content moderation to wildlife conservation, the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally, devel…
Scaling Up LLM Reviews for Google Ads Content Moderation
Wei Qiao, Tushar Dogra, Otilia Stretcu +11
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 Go…