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.

Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs stepwise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 5.7 and 15.0 points. Furthermore, our approach exhibits superiority in terms of efficiency, including run-time, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, validating the strong transferability of our approach.