@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}