mlsanityData to 14 Oct 2026

AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset

arXiv 2505.20826 ↗First posted 27 May 2025cs.CLcited by 2

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Abstract (arXiv)

Identifying factors that make ad text attractive is essential for advertising success. This study proposes AdParaphrase v2.0, a dataset for ad text paraphrasing, containing human preference data, to enable the analysis of the linguistic factors and to support the development of methods for generating attractive ad texts. Compared with v1.0, this dataset is 20 times larger, comprising 16,460 ad text paraphrase pairs, each annotated with preference data from ten evaluators, thereby enabling a more comprehensive and reliable analysis. Through the experiments, we identified multiple linguistic features of engaging ad texts that were not observed in v1.0 and explored various methods for generating attractive ad texts. Furthermore, our analysis demonstrated the relationships between human preference and ad performance, and highlighted the potential of reference-free metrics based on large language models for evaluating ad text attractiveness. The dataset is publicly available at: https://github.com/CyberAgentAILab/AdParaphrase-v2.0.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 45 references that are in the map. First posted on arXiv 27 May 2025.

Builds on

23 of 45 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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11 more
  1. 2024Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilitiescited by 111
  2. 2017A Continuously Growing Dataset of Sentential Paraphrasescited by 72
  3. 2024Detection and Measurement of Syntactic Templates in Generated Textcited by 44
  4. 2023ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustnesscited by 41
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  11. 2025AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Textscited by 5

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Citing papers per year: 2025: 2.

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  1. 2025Distilling Many-Shot In-Context Learning into a Cheat Sheetcited by 2
  2. 2025AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositionscited by 0

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  4. 2026LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responses
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  6. 2025EVALUESTEER: Measuring Reward Model Steerability Towards Values and Preferences
  7. 2025Improving Generative Ad Text on Facebook using Reinforcement Learning
  8. 2025Rewrite-to-Rank: Optimizing Ad Visibility via Retrieval-Aware Text Rewriting