mlsanityData to 14 Oct 2026

Chatting with your ERP: A Recipe

arXiv 2507.23429 ↗First posted 31 Jul 2025cs.AI cs.DB cs.ETcited by 0

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Abstract (arXiv)

This paper presents the design, implementation, and evaluation behind a Large Language Model (LLM) agent that chats with an industrial production-grade ERP system. The agent is capable of interpreting natural language queries and translating them into executable SQL statements, leveraging open-weight LLMs. A novel dual-agent architecture combining reasoning and critique stages was proposed to improve query generation reliability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 31 Jul 2025.

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References · 6

  1. 2023Teaching Large Language Models to Self-Debugcited by 988
  2. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  3. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 119
  4. 2022Evaluating the Text-to-SQL Capabilities of Large Language Modelscited by 101
  5. 2024MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQLcited by 22
  6. 2024SQLFixAgent: Towards Semantic-Accurate Text-to-SQL Parsing via Consistency-Enhanced Multi-Agent Collaborationcited by 11

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