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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.18785 |
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| _version_ | 1866917176141676544 |
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| author | Rajagopalan, Santosh Vronsky, Jonathan Yan, Songbai Golestaneh, S. Alireza Chandra, Shubhra Zhou, Min |
| author_facet | Rajagopalan, Santosh Vronsky, Jonathan Yan, Songbai Golestaneh, S. Alireza Chandra, Shubhra Zhou, Min |
| contents | We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video, and structured data modalities. Through contrastive learning and multi-task optimization, ALF creates unified advertiser representations that capture both content and behavioral patterns. Our model achieves state-of-the-art performance on critical tasks including fraud detection, policy violation identification, and advertiser similarity matching. In production deployment, ALF demonstrates significant real-world impact by delivering simultaneous gains in both precision and recall, for instance boosting recall by over 40 percentage points on one critical policy and increasing precision to 99.8% on another. The architecture's effectiveness stems from its novel combination of multi-modal transformations, inter-sample attention mechanism, spectrally normalized projections, and calibrated probabilistic outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18785 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding Rajagopalan, Santosh Vronsky, Jonathan Yan, Songbai Golestaneh, S. Alireza Chandra, Shubhra Zhou, Min Machine Learning We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video, and structured data modalities. Through contrastive learning and multi-task optimization, ALF creates unified advertiser representations that capture both content and behavioral patterns. Our model achieves state-of-the-art performance on critical tasks including fraud detection, policy violation identification, and advertiser similarity matching. In production deployment, ALF demonstrates significant real-world impact by delivering simultaneous gains in both precision and recall, for instance boosting recall by over 40 percentage points on one critical policy and increasing precision to 99.8% on another. The architecture's effectiveness stems from its novel combination of multi-modal transformations, inter-sample attention mechanism, spectrally normalized projections, and calibrated probabilistic outputs. |
| title | ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.18785 |