arXiv:2607. 06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries.
The paper presents a framework for translating natural‑language queries into Kusto Query Language (KQL) using small language models (SLMs). It introduces lightweight retrieval, error‑aware prompting, LoRA fine‑tuning with rationale distillation, and a two‑stage architecture that pairs an SLM drafter with a low‑cost LLM judge. Evaluations on Microsoft’s NL2KQL Defender dataset show the two‑stage approach achieving high syntax and schema‑valid accuracy while dramatically reducing cost compared to larger LLM baselines.
arXiv:2608. 16663v1 Announce Type: cross Abstract: Direct text-to-SQL asks a language model to do two jobs: interpret the business question and construct the complete relational query.
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.
Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queries, policy failures, cost, and nondeterminism. SemPlan Benchmark evaluates this architectural design space with a deterministic synthetic bilingual benchmark containing 1,800 cases in English and Brazilian Portuguese; 1,200 cases form the frozen scientific evaluation subset.
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...
arXiv:2608. 13612v1 Announce Type: new Abstract: Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queries, policy failures, cost, and nondeterminism.
arXiv:2606. 02109v1 Announce Type: new Abstract: Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approaches fundamentally different from academic benchmarks.
arXiv:2602. 16720v2 Announce Type: replace-cross Abstract: Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments.
text2ql is an open‑source Python framework that enables natural language querying of databases without relying on large language models at query time. It uses a language‑agnostic intermediate representation (QueryIR) and a pluggable renderer to support both SQL and GraphQL targets through a single seven‑stage detection pipeline. In deterministic mode, it achieves 100% execution accuracy with a median latency of 3.2 ms, while the LLM‑backed mode delivers 62‑70% exact match and 84‑91% execution accuracy on benchmark samples.
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:2606. 18108v1 Announce Type: cross Abstract: We develop a text-to-SQL (structured query language) system based on large language models (LLMs) using in-context learning and apply it to the Automatic Learning for the Rapid Classification of Events (ALeRCE) astronomical database.