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    <abstract>Current keyword decision-making in sponsored search advertising relies on large static datasets, limiting automatic keyword setup and failing to adapt to real-time KPI metrics and product updates essential for effective advertising. In this paper, we propose On-the-fly Keyword Generation (OKG), an LLM agent-based method that dynamically monitors KPI changes and adapts keyword generation in real-time, realizing the strategy recommended by advertising platforms. Additionally, we introduce the first publicly accessible dataset containing real keyword data with its KPIs across diverse domains, providing a valuable resource for future research. Experimental results and ablation studies demonstrate the effectiveness of OKG, showing significant improvements across various metrics and emphasizing the importance of each component. We believe OKG not only pioneers the use of LLM agents in this research field but also offers practical value for thousands of advertisers to automate keyword generation in real-world applications.</abstract>
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%0 Conference Proceedings
%T OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising
%A Wang, Zhao
%A Gangopadhyay, Briti
%A Zhao, Mengjie
%A Takamatsu, Shingo
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-okg
%X Current keyword decision-making in sponsored search advertising relies on large static datasets, limiting automatic keyword setup and failing to adapt to real-time KPI metrics and product updates essential for effective advertising. In this paper, we propose On-the-fly Keyword Generation (OKG), an LLM agent-based method that dynamically monitors KPI changes and adapts keyword generation in real-time, realizing the strategy recommended by advertising platforms. Additionally, we introduce the first publicly accessible dataset containing real keyword data with its KPIs across diverse domains, providing a valuable resource for future research. Experimental results and ablation studies demonstrate the effectiveness of OKG, showing significant improvements across various metrics and emphasizing the importance of each component. We believe OKG not only pioneers the use of LLM agents in this research field but also offers practical value for thousands of advertisers to automate keyword generation in real-world applications.
%U https://aclanthology.org/2025.coling-industry.10/
%P 115-127

Markdown (Informal)

[OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising](https://aclanthology.org/2025.coling-industry.10/) (Wang et al., COLING 2025)

• OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising (Wang et al., COLING 2025)

ACL

• Zhao Wang, Briti Gangopadhyay, Mengjie Zhao, and Shingo Takamatsu. 2025. OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 115–127, Abu Dhabi, UAE. Association for Computational Linguistics.