CREATER: Ctr-driven Advertising Text Generation With Controlled Pre-training And Contrastive Fine-tuning

Wei Penghui, Yang Xuanhua, Liu Shaoguo, Wang Liang, Zheng Bo. Arxiv 2022

[Paper]    
Applications Fine Tuning Language Modeling Pretraining Methods RAG Tools Training Techniques

This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). We propose CREATER, a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews. To incorporate CTR objective, our model learns from online A/B test data with contrastive learning, which encourages the model to generate ad texts that obtain higher CTR. To alleviate the low-resource issue, we design a customized self-supervised objective reducing the gap between pre-training and fine-tuning. Experiments on industrial datasets show that CREATER significantly outperforms current approaches. It has been deployed online in a leading advertising platform and brings uplift on core online metrics.

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