Knowledge catalogue
arXiv:2605.22843v1 Announce Type: new Abstract: Text-to-SQL converts natural language questions into executable SQL queries, enabling non-technical users to access relational databases for analytics a
arXiv:2409.02038v3 Announce Type: replace-cross Abstract: Existing text-to-SQL benchmarks have largely been constructed from public databases with well-structured schemas and simplistic question-SQL p
arXiv:2604.21214v1 Announce Type: cross Abstract: Text-to-SQL models have significantly improved with the adoption of Large Language Models (LLMs), leading to their increasing use in real-world applic
arXiv:2510.12831v3 Announce Type: replace Abstract: Multi-turn Text-to-SQL aims to translate a user's conversational utterances into executable SQL while preserving dialogue coherence and grounding to
arXiv:2604.16511v1 Announce Type: cross Abstract: We present SQL Query Engine, an open-source, self-hosted service that translates natural language questions into validated PostgreSQL queries through
arXiv:2602.05385v2 Announce Type: replace Abstract: Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-b
arXiv:2609.00834v1 Announce Type: cross Abstract: Modern Text-to-SQL systems often follow generate-execute-select pipelines, generating multiple candidate queries then selecting the best one. Listwise
arXiv:2608.22830v1 Announce Type: new Abstract: Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables, a
arXiv:2608.22063v1 Announce Type: new Abstract: Agents built on Large Language Models (LLMs) increasingly reach enterprise data through the Model Context Protocol (MCP), and many MCP database servers
arXiv:2607.03991v1 Announce Type: cross Abstract: Repeated LLM calls are the standard way to estimate how trustworthy a Text-to-SQL result is: run the pipeline multiple times, judge each SQL execution
arXiv:2604.00660v2 Announce Type: replace-cross Abstract: Modern data warehouses extend SQL with semantic operators that invoke large language models on each qualifying row, making per-row inference o
arXiv:2607.03501v1 Announce Type: cross Abstract: Copernicus, the European Union's Earth observation program, produces petabytes of Earth observation and climate data, offering immense potential for r
arXiv:2607.03833v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by l
arXiv:2606.31041v1 Announce Type: new Abstract: Natural language-to-SQL (NL2SQL) over real-world enterprise databases remains significantly more challenging than on academic benchmarks. Enterprise sch
arXiv:2606.30851v1 Announce Type: cross Abstract: Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL.
arXiv:2606.03363v1 Announce Type: new Abstract: Text-to-SQL enables natural language access to databases, and recent LLMs have substantially advanced its capabilities. Existing benchmarks such as Spid
arXiv:2606.03327v1 Announce Type: cross Abstract: Text-to-SQL systems are typically evaluated by query-level execution correctness, but this terminal signal provides little guidance about which interm
arXiv:2606.01338v1 Announce Type: new Abstract: Biopharmaceutical manufacturing organizations operate under regulatory frameworks such as FDA guidance, EU Good Manufacturing Practice (GMP), and the EU
arXiv:2605.19010v1 Announce Type: new Abstract: Natural language to SQL (NL2SQL) conversion is an important problem for researchers and enterprises due to the ubiquitous importance of relational datab
arXiv:2605.04719v1 Announce Type: new Abstract: Tool-integrated Text-to-SQL parsing has emerged as a promising paradigm, framing SQL generation as a sequential decision-making process interleaved with
This Databricks blog post discusses modern approaches to SQL-based Extract, Transform, Load (ETL) processes designed for contemporary data platforms, likely covering how SQL ETL tools and practices ar
arXiv:2505.14174v2 Announce Type: replace Abstract: LLMs are effective at code generation tasks like text-to-SQL, but is it worth the cost? Many state-of-the-art approaches use non-task-specific LLM t
arXiv:2609.24186v1 Announce Type: new Abstract: Large language models have achieved remarkable progress on Text-to-SQL through reasoning-enhanced fine-tuning, yet existing approaches predominantly rel
arXiv:2609.25938v1 Announce Type: cross Abstract: A conformal abstention certificate for text-to-SQL is only as truthful as the correctness labels it is calibrated on. The uncertainty pipelines that r
arXiv:2608.29543v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, ofte
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. In enterprise sc
arXiv:2607.20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including
arXiv:2607.11951v1 Announce Type: new Abstract: Large language models can write SQL, but enterprise deployment demands more than plausible text: outputs must be syntactically valid, must respect per-r
To effectively operate and troubleshoot applications, developers and site reliability engineers (SREs) need to understand the full context of their system's behavior, typically as part of their loggin
arXiv:2606.23537v1 Announce Type: cross Abstract: Text-to-SQL enables users to access relational databases via natural language, but real-world settings remain challenging due to coordinated reasoning
SAG retrieves structured multi-hop evidence via event-entity hyperedges and query-time joins, without a maintained global knowledge graph.
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. Real d
arXiv:2606.05634v1 Announce Type: new Abstract: Real-world text-to-SQL is often under-specified until user phrases are grounded in how the database stores values. Prior work attempts to address this b
arXiv:2605.29670v1 Announce Type: cross Abstract: Schema linking is a difficult and important step in large-scale Text-to-SQL, where systems must identify a compact yet sufficient schema context from
arXiv:2605.00628v1 Announce Type: cross Abstract: Text-to-SQL enables non-expert users to query databases in natural language, yet real-world schemas often suffer from ambiguous, abbreviated, or incon
arXiv:2604.28028v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized Text-to-SQL generation, allowing users to query structured data using natural language with growing e
arXiv:2604.22934v1 Announce Type: new Abstract: LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice v
arXiv:2610.03524v1 Announce Type: new Abstract: General-purpose Text-to-SQL systems achieve strong performance on academic benchmarks like Spider and BIRD, where schemas are relatively shallow and col
arXiv:2609.22259v1 Announce Type: new Abstract: Context layers, curated documentation that an analytics agent fetches at query time, produce large accuracy gains on text-to-SQL benchmarks. A with/with
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 o
arXiv:2411.04319v1 Announce Type: cross Abstract: Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large la
arXiv:2608.09588v1 Announce Type: new Abstract: Traditional Text-to-SQL research and benchmarks assume a known target database, overlooking settings in which a query must be routed within a large, het
arXiv:2608.09260v1 Announce Type: cross Abstract: Large language models (LLMs) have advanced Text-to-SQL by enabling natural language interfaces to databases without task-specific fine-tuning. However
arXiv:2601.05451v2 Announce Type: replace-cross Abstract: Recent advances in text-to-SQL have been driven by larger models, better datasets, and new training methods like RLVR. However, progress remai
arXiv:2608.03118v1 Announce Type: new Abstract: This innovative practice full paper describes an AI-powered knowledge graph platform that connects SQL errors to conceptual gaps in undergraduate and gr
arXiv:2608.00693v1 Announce Type: new Abstract: Schema linking is a critical component of Text-to-SQL systems, but existing approaches often trade off contextual modeling capacity, score-based control
I really like to use this one SQL benchmark when testing new models. I had another post some time ago with my benchmarks, but I decided to post a new one because of how well Deepseek did. I like the b
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
arXiv:2606.28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks suc
arXiv:2605.26394v1 Announce Type: new Abstract: Multi-turn Text-to-SQL is central to enterprise analytics yet remains predominantly evaluated in single-turn settings. We introduce EnterpriseMem-Bench,
arXiv:2602.12015v2 Announce Type: replace Abstract: Deploying large language models for clinical Text-to-SQL requires distinguishing two qualitatively different causes of output diversity: (i) input a
arXiv:2605.10176v1 Announce Type: cross Abstract: Natural language interfaces to structured databases are becoming increasingly common, largely due to advances in large language models (LLMs) that ena
arXiv:2605.02815v1 Announce Type: new Abstract: Text-to-SQL over large analytical databases requires navigating complex schemas, resolving ambiguous queries, and grounding decisions in actual data. Mo
arXiv:2602.22699v2 Announce Type: replace-cross Abstract: SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through m
arXiv:2604.13686v1 Announce Type: new Abstract: While Large Language Models (LLMs) have significantly advanced Text-to-SQL performance, existing benchmarks predominantly focus on Western contexts and
arXiv:2601.10398v3 Announce Type: replace Abstract: In LLM-based text-to-SQL systems, unanswerable and underspecified user queries may generate not only incorrect text but also executable programs tha
arXiv:2610.12274v1 Announce Type: new Abstract: Text-to-SQL models are commonly trained to map questions directly to static queries, whereas real-world database agents operate through stateful, multi-
arXiv:2609.30290v1 Announce Type: new Abstract: Production text-to-SQL pipelines often end with an LLM-as-judge whose agreement with human annotators has never actually been measured. When we checked
arXiv:2609.01818v1 Announce Type: cross Abstract: The browser has become a first-class database host: applications increasingly want to store, query, and reason over structured data entirely on the cl
arXiv:2607.22622v1 Announce Type: cross Abstract: Recent Text-to-SQL methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT), achieving substantial gains on complex benchmar