@inproceedings{huang-etal-2025-targa,
    title = "{TARGA}: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data",
    author = "Huang, Xiang  and
      Shen, Jiayu  and
      Huang, Shanshan  and
      Cheng, Sitao  and
      Wang, Xiaxia  and
      Qu, Yuzhong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.137/",
    pages = "2704--2726",
    ISBN = "979-8-89176-251-0",
    abstract = "Semantic parsing, which converts natural language queries into logic forms, plays a crucial role in reasoning within structured environments. However, existing methods encounter two significant challenges: reliance on extensive manually annotated datasets and limited generalization capability to unseen examples. To tackle these issues, we propose Targeted Synthetic Data Generation (Targa), a practical framework that dynamically generates high-relevance synthetic data without manual annotation. Starting from the pertinent entity and relation of a given question, we probe for the potential relevant queries through layer-wise expansion and cross-layer combination. Then, we generate corresponding natural language questions for these constructed queries to jointly serve as the synthetic demonstration for in-context learning. Experiments on multiple knowledge-based question answering (KBQA) datasets demonstrate that Targa, using only a 7B-parameter model, substantially outperforms existing non-fine-tuned methods that utilize close-sourced model, achieving notable improvements in F1 scores on GrailQA(+7.7) and KBQA-Agent(+12.2). Furthermore, Targa also exhibits superior sample efficiency, robustness, and generalization capabilities under non-I.I.D. settings."
}