Year: 2026
Type: article
Abstract: Schema linking, the task of identifying relevant database schema elements (tables and columns) for natural language queries, is a critical component in database-driven natural language interfaces. While existing approaches rely on question decomposition to handle complex queries, they often suffer from error propagation and low precision. In this paper, we propose a novel schema linking framework enhanced by self-verification (SV) and value hints (VHs) that significantly improves both precision and recall. Our approach introduces two key components: (1) self-verification (SV), an iterative refinement mechanism that validates and corrects initial predictions through explicit verification prompts, and (2) value hints (VHs), which explicitly guide the model to recognize database values mentioned in queries. We conduct comprehensive experiments on two benchmark datasets, Spider and BIRD, using two language models of 4B and 80B parameters. Our results demonstrate that SV + VH consistently improves performance across datasets, models, and method configurations, outperforming both decomposition-based approaches and compute-matched alternatives such as self-consistency under equivalent inference budgets.
Source: Big Data and Cognitive Computing
DOI: 10.3390/bdcc10040104
Authors: Linfei Ma, Dexing Wei, Xiangpeng Li, Feng Wen, Haisu Zhang
Institution: National University of Defense Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7147099638