TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data

• URL: http://arxiv.org/abs/2412.19544v1
• Date: Fri, 27 Dec 2024 09:16:39 GMT
• Title: TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data
• Authors: Xiang Huang, Jiayu Shen, Shanshan Huang, Sitao Cheng, Xiaxia Wang, Yuzhong Qu,
• Abstract summary: TARGA is a framework that generates high-relevance synthetic data without manual annotation. It substantially outperforms existing non-fine-tuned methods that utilize close-sourced model. It exhibits superior sample efficiency, robustness, and generalization capabilities under non-I.I.D. settings.
• Score: 9.390415313514762
• License:
• Abstract: Semantic parsing, which converts natural language questions 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 entities and relations 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 demonstrations for in-context learning. Experiments on multiple knowledge base 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.

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