M ulti TEND : A Multilingual Benchmark for Natural Language to N o SQL Query Translation (2025)

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.

## introduction in the age of big data, nosql databases have become indispensable tools for managing vast amounts of unstructured and semi-structured data @xcite . unlike traditional relational databases, nosql databases offer more flexibility in schema design and can handle a wide variety of data types, making them particularly suitable for modern applications such as social media @xcite , e-commerce (nalla and reddy, * yuanfeng song is the corresponding author. 2022), and real-time analytics @xcite . nonetheless, the intricacy and heterogeneity of nosql query languages present a formidable challenge, especially for users who may not have advanced technical skills. to address this challenge, the development of natural language interfaces (nlis) for nosql databases has gained increasing attention. these interfaces are designed to allow users to interact with nosql databases in natural language, thus simplifying access to complex data and lowering the technical barriers. by translating natural language queries (nlqs) into executable nosql queries (i.e., text-to-nosql @xcite ), these systems can significantly enhance user productivity and data accessibility. however, existing natural language to nosql query generation systems and benchmarks have predominantly focused on the english language. this limitation severely restricts the usability of these systems for non-english speakers, who represent a significant portion of the global population. to address above-mentioned issue, we introduce mul-titend, the first multilingual benchmark for natural language to nosql query generation, covering six diverse languages: english, german, french, russian, japanese, and mandarin chinese (sec. 3.1). multi-tend not only expands the scope of natural language to nosql query generation to a multilingual context but also imposes additional challenges to the text-to-nosql tasks. based on the findings from our experiments (sec. 4.2), we categorize the challenges in multi-tend into structural challenge and lexical challenge. in particular, the structural challenge refers to difficulties models face in multilingual intention mapping tasks due to syntactic differences across languages, hindering accurate mapping to nosql operators. additionally, the lexical challenge represents the schema linking difficulties models face in multilingual settings due to lexical differences (e.g., japanese hiragana and katakana, russian cyrillic characters, and morphological variations in german and french) and the complexity of nosql structures (e.g., nested documents and array processing). to tackle these challenges, we propose multilink, a novel framework designed to bridge the gap from multilingual input to nosql query generation. specifically, multitend extracts accurate operator sketches and relevant fields from multilingual nlqs through a parallel linking process, enabling the model to effectively generate high-quality nosql queries even in multilingual contexts. through the incorporation of three novel and specifically designed components, namely intention-aware multilingual data augmentation, parallel multilingual sketch-schema prediction, and retrieval-augmented chain-of-thought query prediction, multilink effectively generates high-quality nosql queries tailored for multilingual scenarios. in summary, our contributions are as follows: ## related work this study is closely related to the fields of text-to-sql and nosql databases, as briefly surveyed below. ## method to address the complex challenge of generating nosql queries from multilingual nlqs, we introduce the innovative multilink framework. this framework leverages a problem decomposition strategy and an efficient parallel linking process to effectively address the challenges of multilingual nosql query generation. in this chapter, we provide a comprehensive overview of multilink. ## conclusion in this work, we introduce multitend, a large-scale multilingual benchmark dataset for text-to-nosql tasks encompassing six languages. to create this dataset, we developed a robust process that combines the capabilities of llms with human efforts. this approach ensures high-quality, semantically aligned, and contextually accurate database fields, nlqs, and nosql queries through thorough manual verification. next, we identify the inherent challenges of multilingual text-to-nosql tasks, including lexical variations and structural inconsistencies across languages. to address these issues, we propose multilink, a unified multilingual pipeline that breaks down the complex task into manageable steps, such as multilingual query augmentation and languagespecific schema linking. extensive experiments demonstrate that multilink excels in generating accurate and semantically consistent nosql queries across multiple languages, significantly outperforming existing baseline models. building on this line of research, we aim to explore additional methodologies for text-to-nosql tasks as the next phase of our work. we anticipate that this work will not only contribute to the ongoing evolution of the nosql field but also inspire further innovations, fostering a dynamic research landscape similar to the advancements seen in the parallel text-to-sql domain.