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: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:2609.22917v1 Announce Type: new Abstract: Non-technical stakeholders frequently cannot write the SQL needed to extract insights from operational databases. We built and evaluated a Text-to-SQL...
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.
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.
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.
SPARQL-LLM is an open‑source, triplestore‑agnostic system that generates SPARQL queries from natural language using lightweight metadata and dedicated components for indexing, prompt building, and execution. It achieves up to 59 % higher F1 scores than the next best system on a multilingual challenge and on bioinformatics knowledge graphs, while being up to 27 × faster and costing no more than $0.01 per question. The project is publicly available on GitHub and is already deployed on real‑world decentralized knowledge graphs such as expasy.org/chat.
The paper presents a multi‑agent, persona‑driven NL2SQL framework that addresses common LLM shortcomings such as schema hallucination, syntax errors, and intent mismatch. It incorporates domain relevance detection, schema‑grounded knowledge construction, KPI‑aware SQL synthesis, syntax‑level self‑repair via sqlite feedback, and semantic consistency checking through SQL‑to‑NL reverse engineering. Evaluated on 60 queries, the system achieves 90% correctness using Mistral across seven coordinated LLM calls, demonstrating a significant improvement in reliability for high‑stakes environments.
arXiv:2510. 15476v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used as interfaces to information, code, and real-world services, making prompt-level security failures a practical concern.
Build2SPARQL is a large-scale benchmark dataset for translating natural-language questions into SPARQL queries over building knowledge graphs. The dataset is generated by a KG‑grounded pipeline that produces 6,136 executable SPARQL queries and 30,680 corresponding natural-language questions across six query-pattern families and five vocabulary registers, covering 201 building KGs. Human validation shows high semantic fidelity, naturalness, and operational plausibility, and retrieval‑augmented evaluation demonstrates significant accuracy gains for open‑weight language models.
The paper introduces AdvGuard, a model‑agnostic framework designed to detect and mitigate query‑based system prompt leakage attacks against large language models. AdvGuard constructs a bounded semantic interaction graph to capture cross‑query relationships, applies statistical filtering, and uses graph convolutional networks for classification, enabling it to identify coordinated extraction attempts across multiple turns. In evaluations, AdvGuard achieves significant improvements in recall and F1 scores over baselines, with negligible false positives, and effectively limits prompt leakage in adaptive attack scenarios.
CVE2AP is an LLM-based system that automatically converts natural language CVE descriptions into PDDL-encoded attack paths. It uses structured prompting and an error‑feedback loop that refines outputs based on planner‑reported syntactic and solvability errors. Empirical tests across various LLMs show high quality results, with up to 86.9% syntax correctness, 78.6% solvability, and 93.1% semantic correctness, and GPT‑5.5 providing the best quality‑cost balance.
The paper introduces Dial, a knowledge‑grounded framework for generating SQL queries that respect the specific dialect of heterogeneous database systems. Dial comprises a Dialect‑Aware Logical Query Planning module, a hierarchical intent‑aware knowledge base (HINT‑KB) containing canonical syntax, function, and constraint repositories, and an execution‑driven debugging loop that separates syntactic recovery from logic auditing. The authors also present DS‑NL2SQL, a benchmark of 2,218 dialect‑specific test cases across six major database systems, and report that Dial improves translation accuracy by 10.25% and dialect feature coverage by 15.77% over existing baselines.