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    <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.</abstract>
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%0 Conference Proceedings
%T TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data
%A Huang, Xiang
%A Shen, Jiayu
%A Huang, Shanshan
%A Cheng, Sitao
%A Wang, Xiaxia
%A Qu, Yuzhong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F huang-etal-2025-targa
%X 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.
%R 10.18653/v1/2025.acl-long.137
%U https://aclanthology.org/2025.acl-long.137/
%U https://doi.org/10.18653/v1/2025.acl-long.137
%P 2704-2726

Markdown (Informal)

[TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data](https://aclanthology.org/2025.acl-long.137/) (Huang et al., ACL 2025)

• TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data (Huang et al., ACL 2025)

ACL

• Xiang Huang, Jiayu Shen, Shanshan Huang, Sitao Cheng, Xiaxia Wang, and Yuzhong Qu. 2025. TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2704–2726, Vienna, Austria. Association for Computational Linguistics.