@inproceedings{chen-etal-2025-towards-boosting,
title = "Towards Boosting {LLM}s-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting",
author = "Chen, Zeyuan and
Wu, Haiyan and
Wu, Kaixin and
Chen, Wei and
Zhong, Mingjie and
Xu, Jia and
Liu, Zhongyi and
Zhang, Wei",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.65/",
pages = "784--793",
abstract = "This paper studies the relevance modeling problem by integrating world knowledge stored in the parameters of LLMs with specialized domain knowledge represented by user behavior data for achieving promising performance. The novel framework ProRBP is proposed, which innovatively develops user-driven behavior neighbor retrieval module to learn domain-specific knowledge in time and introduces progressive prompting and aggregation module for considering diverse aspects of the relevance and prediction stability. We explore an industrial implementation to deploy LLMs to handle full-scale search traffics of Alipay with acceptable cost and latency. The comprehensive experiments on real-world industry data and online A/B testing validate the superiority of our proposal and the effectiveness of its main modules."
}
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<abstract>This paper studies the relevance modeling problem by integrating world knowledge stored in the parameters of LLMs with specialized domain knowledge represented by user behavior data for achieving promising performance. The novel framework ProRBP is proposed, which innovatively develops user-driven behavior neighbor retrieval module to learn domain-specific knowledge in time and introduces progressive prompting and aggregation module for considering diverse aspects of the relevance and prediction stability. We explore an industrial implementation to deploy LLMs to handle full-scale search traffics of Alipay with acceptable cost and latency. The comprehensive experiments on real-world industry data and online A/B testing validate the superiority of our proposal and the effectiveness of its main modules.</abstract>
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%0 Conference Proceedings
%T Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting
%A Chen, Zeyuan
%A Wu, Haiyan
%A Wu, Kaixin
%A Chen, Wei
%A Zhong, Mingjie
%A Xu, Jia
%A Liu, Zhongyi
%A Zhang, Wei
%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 chen-etal-2025-towards-boosting
%X This paper studies the relevance modeling problem by integrating world knowledge stored in the parameters of LLMs with specialized domain knowledge represented by user behavior data for achieving promising performance. The novel framework ProRBP is proposed, which innovatively develops user-driven behavior neighbor retrieval module to learn domain-specific knowledge in time and introduces progressive prompting and aggregation module for considering diverse aspects of the relevance and prediction stability. We explore an industrial implementation to deploy LLMs to handle full-scale search traffics of Alipay with acceptable cost and latency. The comprehensive experiments on real-world industry data and online A/B testing validate the superiority of our proposal and the effectiveness of its main modules.
%U https://aclanthology.org/2025.coling-industry.65/
%P 784-793
Markdown (Informal)
[Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting](https://aclanthology.org/2025.coling-industry.65/) (Chen et al., COLING 2025)
ACL
- Zeyuan Chen, Haiyan Wu, Kaixin Wu, Wei Chen, Mingjie Zhong, Jia Xu, Zhongyi Liu, and Wei Zhang. 2025. Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 784–793, Abu Dhabi, UAE. Association for Computational Linguistics.