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New AptMQL-Bench benchmark highlights challenges in text-to-MQL generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed AptMQL-Bench, a new benchmark for text-to-MQL generation, designed to improve natural language querying of document databases like MongoDB. Existing methods for converting text-to-SQL benchmarks to this format often fail, leading to data loss and inefficient schemas. The new pipeline, utilizing coding agents and human verification, creates MQL queries that are native to MongoDB and preserve data integrity, resulting in a benchmark with 21 databases and over 3,000 queries. Even with advanced models like Claude Opus 4.5, accuracy remains a challenge, highlighting the difficulty of realistic text-to-MQL generation. AI

IMPACT Highlights the ongoing challenges in natural language querying for NoSQL databases, potentially guiding future model development.

RANK_REASON The cluster contains a research paper introducing a new benchmark for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AptMQL-Bench benchmark highlights challenges in text-to-MQL generation

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The cluster contains a research paper introducing a new benchmark for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hy Nguyen, Nabi Rezvani, Robin Vujanic ·

    AptMQL-Bench: From Text-to-SQL to Text-to-MQL via Access-Pattern Schema Design and Data-Preserving Migration

    arXiv:2610.02770v1 Announce Type: new Abstract: Document databases such as MongoDB are core infrastructure for modern applications, and natural-language interfaces to them---text-to-MQL---would let non-experts query complex, semi-structured data without mastering the query langua…

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