Boundary-Aware NL2SQL: Integrating Reliability through Hybrid Reward and Data Synthesis

• URL: http://arxiv.org/abs/2601.10318v1
• Date: Thu, 15 Jan 2026 11:55:01 GMT
• Title: Boundary-Aware NL2SQL: Integrating Reliability through Hybrid Reward and Data Synthesis
• Authors: Songsong Tian, Kongsheng Zhuo, Zhendong Wang, Rong Shen, Shengtao Zhang, Yong Wu,
• Abstract summary: We present BAR- Mutation (Boundary-Aware Reliable NL2), a unified training framework that embeds reliability and boundary awareness directly into the generation process.<n>We employ Knowledge-Grounded Reasoning Synthesis to ensure interpretability.
• Score: 23.501567675008264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present BAR-SQL (Boundary-Aware Reliable NL2SQL), a unified training framework that embeds reliability and boundary awareness directly into the generation process. We introduce a Seed Mutation data synthesis paradigm that constructs a representative enterprise corpus, explicitly encompassing multi-step analytical queries alongside boundary cases including ambiguity and schema limitations. To ensure interpretability, we employ Knowledge-Grounded Reasoning Synthesis, which produces Chain-of-Thought traces explicitly anchored in schema metadata and business rules. The model is trained through a two-stage process: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning via Group Relative Policy Optimization. We design a Task-Conditioned Hybrid Reward mechanism that simultaneously optimizes SQL execution accuracy-leveraging Abstract Syntax Tree analysis and dense result matching-and semantic precision in abstention responses. To evaluate reliability alongside generation accuracy, we construct and release Ent-SQL-Bench, which jointly assesse SQL precision and boundary-aware abstention across ambiguous and unanswerable queries. Experimental results on this benchmark demonstrate that BAR-SQL achieves 91.48% average accuracy, outperforming leading proprietary models, including Claude 4.5 Sonnet and GPT-5, in both SQL generation quality and boundary-aware abstention capability. The source code and benchmark are available anonymously at: https://github.com/TianSongS/BAR-SQL.

Related papers

• APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL [39.76924093980244]
  APEX- verbalize is a framework that shifts the paradigm from passive translation to agentic exploration.<n>Our framework employs a hypothesis-verification loop to ground model reasoning in real data.
  arXiv  Detail & Related papers  (2026-02-11T07:50:47Z)
• Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQL [30.78817492504152]
  HERO is a hierarchical representation approach that integrates global intent and local details.<n>We employ a Nested Message Passing Neural Network (NMPNN) to capture inherent information in relational schema-guided semantics.<n>Our approach outperforms existing state-of-the-art methods, achieving an average 9.40% improvement of AUPRC and 12.35% of AUROC in identifying semantic inconsistencies.<n>It excels at detecting fine-grained semantic errors, provides large language models with more granular feedback, and ultimately enhances the reliability and interpretability of data querying platforms.
  arXiv  Detail & Related papers  (2025-12-28T02:25:33Z)
• Companion Agents: A Table-Information Mining Paradigm for Text-to-SQL [8.159121916366727]
  Large-scale Text-to-curated benchmarks such as BIRD typically assume complete and accurate database annotations as well as available external knowledge.<n>This mismatch substantially limits the real-world applicability of state-of-the-domain Text-to-art systems.<n>We propose a database-centric approach that leverages intrinsic, fine-grained information residing in relational databases to construct missing evidence.
  arXiv  Detail & Related papers  (2025-12-17T07:11:55Z)
• Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation [54.53145282349042]
  We introduce DSR-sourced, a textbfDual-textbfS textbfReasoning framework that models Text-to-context as an interaction between an adaptive context state and a progressive generation state.<n>Without any post-training or in-context examples, DSR-sourced achieves competitive performance, reaching 35.28% execution accuracy on Spider 2.0-Snow and 68.32% on BIRD development set.
  arXiv  Detail & Related papers  (2025-11-26T13:52:50Z)
• HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidance [6.653834890554154]
  We present HES-, a novel hybrid training framework that advances Text-to-latency generation through the integration of thinking-mode-fused supervised fine-tuning.<n>This framework enables switch between reasoning and non-reasoning modes while improving query accuracy and execution efficiency.
  arXiv  Detail & Related papers  (2025-10-10T01:15:57Z)
• Thinkquel: A Model Dedicated to Text-to-dbt Using Synthetic Data and a Span-Aware Objective [2.5297878656953605]
  Thinkquel is a fine-tuned model for producing robust, portable, execution-validated queries.<n>TS-GRPO bridges the gap between token-level training signals and sequence-level execution rewards.
  arXiv  Detail & Related papers  (2025-09-30T19:04:53Z)
• LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion [49.898152180805454]
  Synthetic datasets must maintain domain-specific logical consistency.<n>Existing generative models often overlook these inter-column relationships.<n>This study presents the first method to effectively preserve inter-column relationships without requiring domain knowledge.
  arXiv  Detail & Related papers  (2025-03-04T00:47:52Z)
• Reliable Text-to-SQL with Adaptive Abstention [21.07332675929629]
  We present a novel framework that enhances query generation reliability by incorporating abstention and human-in-the-loop mechanisms.<n>We validate our approach through comprehensive experiments on the BIRD benchmark, demonstrating significant improvements in robustness and reliability.
  arXiv  Detail & Related papers  (2025-01-18T19:36:37Z)
• RSL-SQL: Robust Schema Linking in Text-to-SQL Generation [51.00761167842468]
  We propose a novel framework called RSL- that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on GPT-4ocorrection. Our approach outperforms a series of GPT-4 based Text-to-Seek systems when adopting DeepSeek (much cheaper) with same intact prompts.
  arXiv  Detail & Related papers  (2024-10-31T16:22:26Z)
• SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers [61.48159785138462]
  This paper aims to improve the performance of text-to-dependence by exploring the intrinsic uncertainties in the neural network based approaches (called SUN) Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms competitors and achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-14T06:27:51Z)
• Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing [66.55478402233399]
  We propose a framework to elicit relational structures via a probing procedure based on Poincar'e distance metric. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences. Our framework sets new state-of-the-art performance on three benchmarks.
  arXiv  Detail & Related papers  (2022-06-28T14:05:25Z)
• CREPO: An Open Repository to Benchmark Credal Network Algorithms [78.79752265884109]
  Credal networks are imprecise probabilistic graphical models based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. We present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models.
  arXiv  Detail & Related papers  (2021-05-10T07:31:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.