@inproceedings{chen-etal-2025-llm,
    title = "{LLM}-Enhanced Query Generation and Retrieval Preservation for Task-Oriented Dialogue",
    author = "Chen, Jiale  and
      Dong, Xuelian  and
      Xie, Wenxiu  and
      Peng, Ru  and
      Zeng, Kun  and
      Hao, Tianyong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.737/",
    pages = "14307--14321",
    ISBN = "979-8-89176-256-5",
    abstract = "Knowledge retrieval and response generation are fundamental to task-oriented dialogue systems. However, dialogue context frequently contains noisy or irrelevant information, leading to sub-optimal result in knowledge retrieval. One possible approach to retrieving knowledge is to manually annotate standard queries for each dialogue. Yet, this approach is hindered by the challenge of data scarcity, as human annotation is costly. To solve the challenge, we propose an LLM-enhanced model of query-guided knowledge retrieval for task-oriented dialogue. It generates high-quality queries for knowledge retrieval in task-oriented dialogue solely using low-resource annotated queries. To strengthen the performance correlation between response generation and knowledge retrieval, we propose a retrieval preservation mechanism by further selecting the most relevant knowledge from retrieved top-$K$ records and explicitly incorporating these as prompts to guide a generator in response generation. Experiments on three standard benchmarks demonstrate that our model and mechanism outperform previous state-of-the-art by 3.26{\%} on average with two widely used evaluation metrics."
}