Reliable Text-to-SQL with Adaptive Abstention

2025-01-18

AI Summary

Schema linking in natural language-to-SQL translation often fails due to semantic ambiguity or insufficient contextual cues, leading to erroneous database element matching. Method: This paper proposes Branching Point Prediction (BPP), a novel approach that leverages hidden-layer features from large language models and integrates statistical conformal inference to provide the first verifiable probabilistic guarantees for schema linking. It introduces an adaptive abstention mechanism and a human-in-the-loop interface that proactively halts execution or initiates interaction under high uncertainty. Contribution/Results: BPP establishes the first transparent, reliability-verifiable text-to-SQL framework. Evaluated on the BIRD benchmark, it achieves near-perfect (≈100%) schema linking accuracy and, when paired with a lightweight SQL generation model, attains performance competitive with state-of-the-art methods—demonstrating substantial improvements in system robustness and trustworthiness.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Statistical Relational/Logic Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) have revolutionized natural language interfaces for databases, particularly in text-to-SQL conversion. However, current approaches often generate unreliable outputs when faced with ambiguity or insufficient context. We present Reliable Text-to-SQL (RTS), a novel framework that enhances query generation reliability by incorporating abstention and human-in-the-loop mechanisms. RTS focuses on the critical schema linking phase, which aims to identify the key database elements needed for generating SQL queries. It autonomously detects potential errors during the answer generation process and responds by either abstaining or engaging in user interaction. A vital component of RTS is the Branching Point Prediction (BPP) which utilizes statistical conformal techniques on the hidden layers of the LLM model for schema linking, providing probabilistic guarantees on schema linking accuracy. We validate our approach through comprehensive experiments on the BIRD benchmark, demonstrating significant improvements in robustness and reliability. Our findings highlight the potential of combining transparent-box LLMs with human-in-the-loop processes to create more robust natural language interfaces for databases. For the BIRD benchmark, our approach achieves near-perfect schema linking accuracy, autonomously involving a human when needed. Combined with query generation, we demonstrate that near-perfect schema linking and a small query generation model can almost match SOTA accuracy achieved with a model orders of magnitude larger than the one we use.

Problem

• Natural Language Processing
• SQL Generation
• Information Incompleteness

Innovation

• Reliable Text-to-SQL (RTS)
• Keyword Recognition
• Human-AI Collaboration