MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation

TL;DR

This paper presents MultiTEND, a comprehensive multilingual benchmark for translating natural language to NoSQL queries across six languages, and introduces MultiLink, a framework that significantly improves translation accuracy by addressing linguistic challenges.

Contribution

The paper introduces MultiTEND, the first large-scale multilingual benchmark for NL to NoSQL translation, and proposes MultiLink, a novel framework that enhances multilingual query generation accuracy.

Findings

Performance gaps of 4%-6% between English and non-English languages.

MultiLink improves accuracy by about 15% in English and 10% on average for other languages.

MultiTEND reveals diverse linguistic challenges in NL to NoSQL translation.

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.