arXiv AI
6d ago
DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation
arXiv:2608. 11889v1 Announce Type: cross Abstract: Prompting-based (\textit{i}.
What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study
arXiv:2607. 06799v1 Announce Type: cross Abstract: Evaluating uncertainty in AI-generated SQL queries requires estimating whether a query is correct, where correct means it executes to the same result as a human-written reference.
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Hugging Face Trending Papers
Jun 29
How Far Do On-Prem Open LLMs Get on Text-to-SQL? A Cross-Family Size x Technique Frontier on BIRD
Organizations that cannot send data to a cloud API increasingly ask: how good is Text-to-SQL if the model must run on-premises on open weights, and which popular accuracy "recipes" are worth their compute? We answer with an honest, fully reproducible benchmark on the BIRD development split (n=1534, Execution Accuracy), evaluating three open model families across two generations -- Qwen2.
arXiv AI
Jul 24
ReliableTableQA:How Much Supervision Does Reliability Annotation Need?
arXiv:2607. 20537v1 Announce Type: cross Abstract: We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether the query is answerable, but whether the computed answer is statistically meaningful.
arXiv Machine Learning
Jun 30
How Far Do On-Prem Open LLMs Get on Text-to-SQL? A Cross-Family Size x Technique Frontier on BIRD
arXiv:2606. 29733v1 Announce Type: cross Abstract: Organizations that cannot send data to a cloud API increasingly ask: how good is Text-to-SQL if the model must run on-premises on open weights, and which popular accuracy "recipes" are worth their compute?
arXiv AI
2d ago
Never the Number: Structural Abstention for AI Systems Whose Answers Are Consumed as Fact
arXiv:2608. 13926v1 Announce Type: new Abstract: Large language models have made natural language interfaces to databases (NLIDB) newly credible, but LLM text-to-SQL systems fail in a way that matters for deployment: a hallucinated column or a mis-aggregated total yields a fluent wrong answer, indistinguishable at the point of use from a right one.
arXiv AI
Jul 7
TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data
arXiv:2607. 03926v1 Announce Type: cross Abstract: Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows.
arXiv AI
Jun 11
TAHOE: Text-to-SQL with Automated Hint Optimization from Experience
arXiv:2606. 12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult.
arXiv AI
Jun 30
Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval
arXiv:2606. 30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query.
arXiv AI
Jul 28
CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking process
arXiv:2607. 22624v1 Announce Type: new Abstract: Recently, there have been several works in the Text-to-SQL domain that utilize Small Language Models (SLMs) for training.
arXiv AI
Jul 14
Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces
arXiv:2604. 11996v2 Announce Type: replace-cross Abstract: Should we trust Large Language Models (LLMs) with high accuracy?
arXiv AI
Jun 2
SIRIUS-SQL: Anchoring Multi-Candidate Text-to-SQL in Execution Feedback
arXiv:2606. 01246v1 Announce Type: new Abstract: Text-to-SQL on complex schemas is unreliable on a single pass, so recent systems generate multiple SQL candidates and let voting filter out errors.
arXiv AI
Jun 9
Fast LLM-Based Semantic Filtering: From a Unified Framework to an Adaptive Two-Phase Method
arXiv:2606. 08090v1 Announce Type: cross Abstract: Evaluating a natural-language yes/no predicate over a document corpus under an accuracy target - the semantic filter - is a cornerstone of LLM-based data processing.