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

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-finetuned 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.