@inproceedings{qin-etal-2025-multitend,
    title = "{M}ulti{TEND}: A Multilingual Benchmark for Natural Language to {N}o{SQL} Query Translation",
    author = "Qin, Zhiqian  and
      Song, Yuanfeng  and
      Lu, Jinwei  and
      Song, Yuanwei  and
      Li, Shuaimin  and
      Zhang, Chen Jason",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1265/",
    pages = "24632--24657",
    ISBN = "979-8-89176-256-5",
    abstract = "Natural language interfaces for NoSQL databases are increasingly vital in the big data era, enabling users to interact with complex, unstructured data without deep technical expertise. However, most recent advancements focus on English, leaving a gap for multilingual support. This paper introduces MultiTEND, the first and largest multilingual benchmark for natural language to NoSQL query generation, covering six languages: English, German, French, Russian, Japanese and Mandarin Chinese.Using MultiTEND, we analyze challenges in translating natural language to NoSQL queries across diverse linguistic structures, including lexical and syntactic differences. Experiments show that performance accuracy in both English and non-English settings remains relatively low, with a 4{\%}-6{\%} gap across scenarios like fine-tuned SLM, zero-shot LLM, and RAG for LLM.To address the aforementioned challenges, we introduce MultiLink, a novel framework that bridges the multilingual input to NoSQL query generation gap through a Parallel Linking Process. It breaks down the task into multiple steps, integrating parallel multilingual processing, Chain-of-Thought (CoT) reasoning, and Retrieval-Augmented Generation (RAG) to tackle lexical and structural challenges inherent in multilingual NoSQL generation. MultiLink shows enhancements in all metrics for every language against the top baseline, boosting execution accuracy by about 15{\%} for English and averaging a 10{\%} improvement for non-English languages."
}