# MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation Source: [MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation]() Collection index: [llms.txt]() Data updated: 2026-01-02T08:48:40.444Z Authors: Zhiqian Qin, Yuanfeng Song, Jinwei Lu, Yuanwei Song, Shuaimin Li, Chen Jason Zhang Published: 2025-02-16T07:12:47.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multilingual Table Question Answering on MultiTEND EN](): Multilingual Table Question Answering; MultiTEND EN; EM: 30.05. - [Multilingual Table Question Answering on MultiTEND ZH (Chinese)](): Multilingual Table Question Answering; MultiTEND ZH (Chinese); EM: 23.47. - [Multilingual Table Question Answering on MultiTEND French](): Multilingual Table Question Answering; MultiTEND French; EM: 25.58. - [Multilingual Table Question Answering on MultiTEND German](): Multilingual Table Question Answering; MultiTEND German; EM (Exact Match): 25.65. - [Multilingual Table Question Answering on MultiTEND Japanese](): Multilingual Table Question Answering; MultiTEND Japanese; EM: 23.53. - [Multilingual Table Question Answering on MultiTEND Russian](): Multilingual Table Question Answering; MultiTEND Russian; EM (Exact Match): 24.95. - [Text-to-NoSQL on MultiTEND Avg of 6 langs](): Text-to-NoSQL; MultiTEND Avg of 6 langs; Exact Match (EM): 25.54. - [Text-to-NoSQL on MultiTEND average of 6 languages (test)](): Text-to-NoSQL; MultiTEND average of 6 languages (test); Exact Match (EM): 25.54. ## Original sources and implementation [Original paper]() [PDF]()