TY - GEN
T1 - MultiTEND
T2 - 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025
AU - Qin, Zhiqian
AU - Song, Yuanfeng
AU - Lu, Jinwei
AU - Song, Yuanwei
AU - Li, Shuaimin
AU - Zhang, Chen Jason
N1 - Publisher Copyright:
© 2025 Association for Computational Linguistics.
PY - 2025/7
Y1 - 2025/7
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105028589065
U2 - 10.18653/v1/2025.findings-acl.1265
DO - 10.18653/v1/2025.findings-acl.1265
M3 - Conference article published in proceeding or book
AN - SCOPUS:105028589065
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 24632
EP - 24657
BT - Findings of the Association for Computational Linguistics
A2 - Che, Wanxiang
A2 - Nabende, Joyce
A2 - Shutova, Ekaterina
A2 - Pilehvar, Mohammad Taher
PB - Association for Computational Linguistics (ACL)
Y2 - 27 July 2025 through 1 August 2025
ER -