arXiv AI
SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation
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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 AI
Sep 1
BIRD-History: A Benchmark for History-Driven Text-to-SQL with Fine-Grained Knowledge Annotations
arXiv:2608.29345v1 Announce Type: new Abstract: While recent Large Language Model (LLM)-based text-to-SQL systems achieve impressive performance on standard benchmarks, they struggle when user querie...
arXiv AI
Sep 21
The Stochastic Shift: A New Evaluation Paradigm for Text-to-SQL with AI Operators
arXiv:2609.21133v1 Announce Type: cross Abstract: SQL has been augmented with AI operators, enabling modern data analytics platforms to derive insights from both structured and unstructured data. We...
arXiv AI
Aug 18
ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models
arXiv:2608. 15145v1 Announce Type: new Abstract: Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines.
arXiv Computation and Language
3d ago
GRIPS: An Empirical Study of Pre-SQL Grounding Information for SQL Generation
The paper introduces GRIPS, a three‑stage method that extracts structured pre‑SQL grounding information—such as table, column, value, and relation links—to guide text‑to‑SQL generation. Experiments on TempTabQA‑C and BIRD show that GRIPS‑Adaptive outperforms baseline SQL Static in 18 of 20 settings, boosting average accuracy by 11.8 percentage points and cutting model‑call token usage by 47.8%. The study demonstrates that explicit grounding improves accuracy and efficiency, while adding more pre‑SQL data does not always help.
arXiv Machine Learning
Sep 11
Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)
The paper introduces EXYGEN, a framework that enables conversational access to large knowledge graphs by combining VoID descriptions, ShEx schemas, retrieved triples, and example question‑query pairs in a retrieval‑augmented generation pipeline. On the SciQA benchmark, this approach achieves an exact‑match score of 0.419 without fine‑tuning any large language model, and shows that larger general‑purpose LLMs can outperform smaller code‑specialized ones when provided sufficient context. To scale metadata generation for very large KGs, the authors propose a predicate‑coverage‑aware parallel graph sampling strategy that preserves structural diversity, reduces runtime by over 80× on OpenCitations Meta and GESIS, and is the only tractable method for obtaining complete metadata on ORKG.