#text-to-sql News & Analysis

6 articles tagged with #text-to-sql. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AINeutralarXiv – CS AI · Apr 156/10

🧠

LatentRefusal: Latent-Signal Refusal for Unanswerable Text-to-SQL Queries

Researchers propose LatentRefusal, a safety mechanism for LLM-based text-to-SQL systems that detects unanswerable queries by analyzing intermediate hidden activations rather than relying on output-level instruction following. The approach achieves 88.5% F1 score across four benchmarks while adding minimal computational overhead, addressing a critical deployment challenge in AI systems that generate executable code.

AIBullisharXiv – CS AI · Mar 96/10

🧠

CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain

Researchers introduce CBR-to-SQL, a new framework using Case-Based Reasoning to improve natural language-to-SQL translation for healthcare databases. The system addresses limitations of standard RAG approaches by using two-stage retrieval and abstract case templates, achieving state-of-the-art results on medical datasets.

AIBullisharXiv – CS AI · Feb 276/107

🧠

Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL

Researchers propose Struct-SQL, a knowledge distillation framework that improves Small Language Models for Text-to-SQL tasks by using structured Chain-of-Thought reasoning instead of unstructured approaches. The method achieves an 8.1% improvement over baseline distillation, primarily by reducing syntactic errors through formal query execution plan blueprints.

AINeutralarXiv – CS AI · Mar 124/10

🧠

EvoSchema: Towards Text-to-SQL Robustness Against Schema Evolution

Researchers introduce EvoSchema, a comprehensive benchmark to test how well text-to-SQL AI models handle database schema changes over time. The study reveals that table-level changes significantly impact model performance more than column-level modifications, and proposes training methods to improve model robustness in dynamic database environments.

AINeutralarXiv – CS AI · Mar 54/10

🧠

SpotIt+: Verification-based Text-to-SQL Evaluation with Database Constraints

SpotIt+ is a new open-source tool that evaluates Text-to-SQL systems through verification-based testing, actively searching for database instances that reveal differences between generated and ground truth SQL queries. The tool incorporates constraint-mining that combines rule-based specification mining with LLM validation to generate more realistic test scenarios.

AINeutralarXiv – CS AI · Mar 54/10

🧠

SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verification

Researchers introduce SpotIt, a new evaluation method for Text-to-SQL systems that uses formal verification to find database instances where generated queries differ from ground-truth queries. Testing on the BIRD dataset revealed that current test-based evaluation methods often miss differences between generated and correct SQL queries.