Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation

• URL: http://arxiv.org/abs/2510.24762v1
• Date: Thu, 23 Oct 2025 03:35:00 GMT
• Title: Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation
• Abstract summary: Falcon is a cross-domain Chinese text-to-comparator benchmark grounded in an enterprise-compatible dialect (Max/Hive)<n>It contains 600 Chinese questions over 28 databases; 77% require multi-table reasoning and over half touch more than four tables.<n>For evaluation, we release a robust execution comparator and an automated evaluation pipeline.
• Score: 14.191844670418568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Falcon, a cross-domain Chinese text-to-SQL benchmark grounded in an enterprise-compatible dialect (MaxCompute/Hive). It contains 600 Chinese questions over 28 databases; 77% require multi-table reasoning and over half touch more than four tables. Each example is annotated along SQL-computation features and Chinese semantics. For evaluation, we release a robust execution comparator and an automated evaluation pipeline, under which all current state-of-the-art large-scale models (including Deepseek) achieve accuracies of at most 50%. Major errors originate from two sources: (1) schema linking in large enterprise landscapes - hundreds of tables, denormalized fields, ambiguous column names, implicit foreign-key relations and domain-specific synonyms that make correct join/column selection difficult; and (2) mapping concise, colloquial Chinese into the exact operators and predicates required for analytics - e.g., choosing the correct aggregation and group-by keys, expressing time windows and granularities, applying unit conversions, handling NULLs and data-quality rules, and formulating nested or windowed subqueries. Falcon therefore targets Chinese-specific semantics and enterprise dialects (abbreviations, business jargon, fuzzy entity references) and provides a reproducible middle ground before full production deployment by using realistic enterprise schemas, query templates, an execution comparator, and an automated evaluation pipeline for end-to-end validation.

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