The proliferation of multi-modal online advertisements necessitates robust content moderation to ensure user safety, as offensive ad content can cause user distress and erode platform trust. This paper addresses the detection of content that becomes offensive only when a user’s search query is paired with a specific ad, a context-dependent challenge that simple moderation often misses. Key challenges include the nuanced, multi-modal nature of ads, severe data scarcity and class imbalance due to the rarity of offensive content, and the high cost of human labeling. To overcome these limitations, we introduce a novel, context-aware detection framework centered on a large-scale, Multi-modal Teacher-Student Knowledge Distillation architecture. A powerful Gemini encoder-only “teacher” model distills its knowledge into a lightweight student model suitable for low-latency deployment. We enhance robustness using a novel graph mining technique to find rare offensive examples for training. For evaluation, we developed a highly accurate Automated Evaluation Model (AEM)—a separate, larger Gemini model utilizing Chain-of-Thought (CoT) reasoning—to rigorously assess performance in a live A/B test. Our results demonstrate that the proposed framework reduces the serving of offensive query-ad pairs by more than 80% compared to the baseline, while maintaining the efficiency required for real-time advertising systems that operate at a scale of over ≈100 billion query-ad pairs per day. Disclaimer: This paper contains sentences and images that may be offensive. These examples are included solely for scientific analysis and do not reflect the views of the authors.

Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user-specified intentions. Nevertheless, existing benchmarks for evaluating MLLMs’ instruction-following capability primarily focus on verbal instructions in the textual modality. These limitations hinder a thorough analysis of instruction-following capabilities, as they overlook the implicit constraints embedded in the semantically rich visual modality. To address this gap, we introduce VC-IFEval, a new benchmark accompanied by a systematically constructed dataset that evaluates MLLMs’ instruction-following ability under multimodal settings. Our benchmark systematically incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions. Furthermore, by fine-tuning MLLMs on our dataset, we achieve substantial gains in visual instruction-following accuracy and adherence. Through extensive evaluation across representative MLLMs, we provide new insights into the strengths and limitations of current models.