AgentNLQ: A General-Purpose Agent for Natural Language to SQL

• URL: http://arxiv.org/abs/2605.19010v1
• Date: Mon, 18 May 2026 18:33:15 GMT
• Title: AgentNLQ: A General-Purpose Agent for Natural Language to SQL
• Abstract summary: This study presents a new multi-agent method for NL2 that achieves 78.1% semantic accuracy on the BIg Bench for LaRge-scale Database benchmark.
• Score: 2.224371215696068
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language to SQL (NL2SQL) conversion is an important problem for researchers and enterprises due to the ubiquitous importance of relational databases in broad-ranging practical problems. Despite the rapid advancements in the capabilities of LLMs, NL2SQL has not reached parity in accuracy with human expert SQL writers, hence needing additional improvements in NL2SQL algorithms. This study presents a new multi-agent method for NL2SQL that achieves 78.1% semantic accuracy on the BIg Bench for LaRge-scale Database (BIRD) benchmark. Our method leverages a semantically enriched representation of user-provided schema, adds user-provided business rules, and produces accurate SQL queries. The main contributions of this study are (a) We designed an optimized new orchestrator in a multi-agent solution that uses LLMs to plan, orchestrate, reflect, and self-correct to generate accurate SQL queries, (b) We developed an advanced schema enrichment method that creates context-aware metadata to improve accuracy, and (c) We demonstrated the accuracy and generalizability of the method across different domains and datasets by evaluating it on the BIRD-SQL benchmark.

Related papers

• Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows [54.49251107006261]
  We introduce Spider 2.0-AIFunc, a benchmark of 465 verified instances across 125 real-world databases covering six types of AI functions on the Snowflake platform.<n>We find that the strongest proprietary models reach 67-70% execution accuracy while the best open-source model achieves 58.1%.
  arXiv  Detail & Related papers  (2026-07-07T12:54:08Z)
• NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions [16.53346245559808]
  Large language models (LLMs) have greatly improved NL2 algorithms, but their rapid development outpaces systematic evaluation.<n>We present NL2Bench, the first modular evaluation and benchmarking framework for unified-enabled NL2 approaches.<n>Our evaluation reveals significant gaps in existing NL2 methods, highlighting not only substantial room for accuracy improvements but also the significant computational inefficiency.
  arXiv  Detail & Related papers  (2026-04-13T18:00:05Z)
• LLM-Based SQL Generation: Prompting, Self-Refinement, and Adaptive Weighted Majority Voting [7.590911146338215]
  We propose a Single-Agent Self-Refinement with Ensemble Voting (SSEV)<n>We build on insights from the SSEV pipeline to address the growing complexity of enterprise databases and real-world Text-to-Act tasks.<n>ReCAPAgent-5.5% integrates specialized agents for planning, external knowledge retrieval, critique, action generation, self-refinement, schema linking, and result validation.
  arXiv  Detail & Related papers  (2026-01-25T18:38:58Z)
• End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation [6.5390580456423555]
  Traditional approaches model text-to- query as a direct translation task.<n>Recent advances in large language models (LLMs) have significantly improved translation accuracy.<n>We propose a three-stage end-to-end text-to-end framework to identify the user's intended database.
  arXiv  Detail & Related papers  (2025-08-08T15:16:36Z)
• RAISE: Reasoning Agent for Interactive SQL Exploration [47.77323087050061]
  We propose a novel framework that unifies schema linking, query generation, and iterative refinement within a single, end-to-end component.<n>Our method emulates how humans answer questions when working with unfamiliar databases.
  arXiv  Detail & Related papers  (2025-06-02T03:07:08Z)
• Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation [25.638927795540454]
  We introduce the Text-to-No task, which aims to convert natural language queries into accessible queries.<n>To promote research in this area, we released a large-scale and open-source dataset for this task, named TEND (short interfaces for Text-to-No dataset)<n>We also designed a SLM (Small Language Model)-assisted and RAG (Retrieval-augmented Generation)-assisted multi-step framework called SMART, which is specifically designed for Text-to-No conversion.
  arXiv  Detail & Related papers  (2025-02-16T17:01:48Z)
• RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL [48.516004807486745]
  Large language models (LLMs) with in-context learning have significantly improved the performance of text-to- task. We propose RB-, a novel retrieval-based framework for in-context prompt engineering. Experiment results demonstrate that our model achieves better performance than several competitive baselines on public datasets BIRD and Spider.
  arXiv  Detail & Related papers  (2024-07-11T08:19:58Z)
• PURPLE: Making a Large Language Model a Better SQL Writer [14.627323505405327]
  We propose PURPLE, which improves accuracy by retrieving demonstrations containing the requisite logical operator composition for the NL2 task. PURPLE achieves a new state-of-the-art performance of 80.5% exact-set match accuracy and 87.8% execution match accuracy on the validation set of the popular NL2 benchmark.
  arXiv  Detail & Related papers  (2024-03-29T07:01:29Z)
• MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL [47.120862170230566]
  Recent Text-to-yourself methods usually suffer from significant performance degradation on "huge" databases.<n>We introduce MAC, a novel Text-to-yourself LLM-based multi-agent collaborative framework.<n>In our framework, we leverage GPT-4 as the strong backbone for all agent tasks to determine the upper bound of our framework.<n>We then fine-tune an open-sourced instruction-followed model,sql-Llama, by leveraging Code 7B, to accomplish all tasks as GPT-4 does.
  arXiv  Detail & Related papers  (2023-12-18T14:40:20Z)
• Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation [76.76046657162306]
  Large language models (LLMs) have emerged as a new paradigm for Text-to- task. Large language models (LLMs) have emerged as a new paradigm for Text-to- task.
  arXiv  Detail & Related papers  (2023-08-29T14:59:54Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.