5 papers
Offline-to-Online Creative Optimization with Generative Models and Adaptive Testing
Kevin Lee, Benjamin Letham, Zhiyuan Jerry Lin +5
Ad creative optimization is increasingly constrained by evaluation rather than generation. Generative models can produce many plausible creatives, but reliable evaluation requires…
Decoding the Hook: A Multimodal LLM Framework for Analyzing the Hooking Period of Video Ads
Kunpeng Zhang, Poppy Zhang, Shawndra Hill +1
Video-based ads are a vital medium for brands to engage consumers, with social media platforms leveraging user data to optimize ad delivery and boost engagement. A crucial but unde…
MLLM-VADStory: Domain Knowledge-Driven Multimodal LLMs for Video Ad Storyline Insights
Jasmine Yang, Poppy Zhang, Shawndra Hill
We propose MLLM-VADStory, a novel domain knowledge-guided multimodal large language models (MLLM) framework to systematically quantify and generate insights for video ad storyline…
LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
Yuanchen Wu, Saurabh Verma, Justin Lee +6
Large language models (LLMs) are highly sensitive to prompts, but most automatic prompt optimization (APO) methods assume access to ground-truth references (e.g., labeled validatio…
Characterizing and Minimizing Divergent Delivery in Meta Advertising Experiments
Gordon Burtch, Robert Moakler, Brett R. Gordon +2
Many digital platforms offer advertisers experimentation tools like Meta's Lift and A/B tests to optimize their ad campaigns. Lift tests compare outcomes between users eligible to…