arXiv AI
BIRD-History: A Benchmark for History-Driven Text-to-SQL with Fine-Grained Knowledge Annotations
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arXiv AI
Sep 4
Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL
Reflect‑SQL is a new framework for converting natural language into SQL queries. It tackles challenges such as large, obscure database schemas, poor table and column retrieval, and syntactically or logically flawed SQL by using a multi‑stage self‑reflection approach. The system iteratively refines queries and SQL through feedback loops driven by an LLM‑as‑a‑judge, achieving 72.03% execution accuracy on the BIRD benchmark, outperforming existing baselines.
arXiv AI
Jun 16
Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning
arXiv:2606. 15598v1 Announce Type: new Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively.
arXiv AI
Sep 10
SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation
arXiv:2609.08950v1 Announce Type: cross Abstract: Text-to-SQL systems translate natural language queries into executable SQL, democratizing access to structured data. Despite recent advances driven b...
Hugging Face Trending Papers
Jul 14
Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval
Retrieval in the SQL setting has largely been studied as the task of finding, within a large collection of SQL statements, the statement that answers a natural-language question. At scale, however, a more fundamental retrieval problem precedes generation: schema retrieval, identifying the tables and columns a question requires in a database that may contain thousands of them, far more than fit in a model's context.
arXiv AI
Jun 9
UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL
arXiv:2606. 08018v1 Announce Type: new Abstract: Existing text-to-SQL benchmarks are largely centered on SQLite, making it difficult to evaluate whether models can generalize across heterogeneous SQL dialects.
arXiv Machine Learning
Oct 5
FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
arXiv:2610.03625v1 Announce Type: cross Abstract: Relational databases are among the most widely deployed forms of structured knowledge, and natural language access to them requires grounding languag...