mlsanityData to 14 Oct 2026

CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning

arXiv 2205.08943 ↗First posted 18 May 2022cs.CL cs.IRcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 18 May 2022.

Builds on

11 of 24 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
  6. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
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  8. 2021CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarizationcited by 120
  9. 2021Contrastive Learning for Many-to-many Multilingual Neural Machine Translationcited by 108
  10. 2020Contrastive Learning with Adversarial Perturbations for Conditional Text Generationcited by 56
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Citing papers per year: 2023: 3; 2024: 2; 2025: 5; 2026: 7.

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  1. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  2. 2023Striking Gold in Advertising: Standardization and Exploration of Ad Text Generationcited by 17
  3. 2025CTR-Driven Advertising Image Generation with Multimodal Large Language Modelscited by 14
  4. 2023Natural Language Generation for Advertising: A Surveycited by 10
  5. 2024Step-Back Profiling: Distilling User History for Personalized Scientific Writingcited by 10
  6. 2025AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Textscited by 5
  7. 2025LLM-driven Constrained Copy Generation through Iterative Refinementcited by 3
  8. 2025HLLM-Creator: Hierarchical LLM-based Personalized Creative Generationcited by 3
  9. 2026RELATE: A Reinforcement Learning-Enhanced LLM Framework for Advertising Text Generationcited by 2
  10. 2026Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Modelscited by 1
  11. 2026Interactor: Agentic RL oriented Iterative Creation for Ad Description Generation in Sponsored Searchcited by 1
  12. 2024GCOF: Self-iterative Text Generation for Copywriting Using Large Language Modelcited by 0
5 more
  1. 2025AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositionscited by 0
  2. 2026Recontextualizing Famous Quotes for Brand Slogan Generationcited by 0
  3. 2026AgenticGen: Reward-Guided Agentic Video Generation for Advertisingcited by 0
  4. 2026How Can Recommendation Feedback Evolve Agent Memory?cited by 0
  5. 2026Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignmentcited by 0