# ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding

arXiv: https://arxiv.org/abs/2504.18785 · HTML: https://mlsanity.com/paper/2504.18785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 45 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Builds on (most cited first, 30 of 30)

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- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [FLAVA: A Foundational Language And Vision Alignment Model](https://mlsanity.com/paper/2112.04482/) (2021)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
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- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [OmniVL:One Foundation Model for Image-Language and Video-Language Tasks](https://mlsanity.com/paper/2209.07526/) (2022)
- [Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning](https://mlsanity.com/paper/2106.02584/) (2021)
- [One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers](https://mlsanity.com/paper/1906.02773/) (2019)
- [Automatic Understanding of Image and Video Advertisements](https://mlsanity.com/paper/1707.03067/) (2017)
- [OmniPred: Language Models as Universal Regressors](https://mlsanity.com/paper/2402.14547/) (2024)
- [Learning to Remove: Towards Isotropic Pre-trained BERT Embedding](https://mlsanity.com/paper/2104.05274/) (2021)
- [Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum](https://mlsanity.com/paper/2312.06441/) (2023)
- [Graph-RISE: Graph-Regularized Image Semantic Embedding](https://mlsanity.com/paper/1902.10814/) (2019)
- [The Vizier Gaussian Process Bandit Algorithm](https://mlsanity.com/paper/2408.11527/) (2024)
- [Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://mlsanity.com/paper/2212.02934/) (2022)
- [Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization](https://mlsanity.com/paper/2411.15931/) (2024)
- [Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval](https://mlsanity.com/paper/2303.08599/) (2023)
- [The Performance of Sequential Deep Learning Models in Detecting Phishing Websites Using Contextual Features of URLs](https://mlsanity.com/paper/2404.09802/) (2024)
- [Using Foundation Models to Detect Policy Violations with Minimal Supervision](https://mlsanity.com/paper/2306.06234/) (2023)
- [Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection](https://mlsanity.com/paper/2411.19457/) (2024)

## Abstract (arXiv)

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.