AI BENCHMARK ANALYSIS
IndicDB - Benchmarking Multilingual Text-to-SQL Capabilities in Indian Languages
IndicDB is a comprehensive multilingual Text-to-SQL benchmark for Indian languages, addressing gaps in existing English-centric benchmarks. It uses real-world Indian administrative data, complex relational schemas, and a three-agent judge pattern for synthesis. The benchmark involves 15,617 tasks across seven languages, evaluating state-of-the-art LLMs. Results show a 9% performance drop from English to Indic languages, driven by schema-linking difficulties and structural ambiguity, highlighting a persistent 'Indic Gap'. External evidence augmentation significantly improves performance.
Key Impact Metrics
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Databases Curated
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Total Tasks Synthesized
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Avg. Performance Drop (English to Indic)
Deep Analysis & Enterprise Applications
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Agentic Schema Synthesis
IndicDB constructs 20 PostgreSQL databases (237 tables, 7.69M rows) using a novel three-agent judge pattern (Architect, Auditor, Refiner). This process transforms denormalized government data into complex star/snowflake schemas with join-depths up to six, ensuring structural rigor and high relational density.
Enterprise Process Flow
Architect Synthesizes Schemas
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Auditor Validates Architecture
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Refiner Finalizes Schema & Data Typing
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Manual Verification & Bulk Load
Multilingual Expansion & Verification
English tasks are expanded into six Indic language variants (Hindi, Bengali, Tamil, Telugu, Marathi, Hinglish) using an English-First approach and Gemini 3 Flash for translation. A multi-stage Human-in-the-Loop (HITL) framework, utilizing COMET scores and expert linguistic audits, ensures cross-lingual semantic equivalence and addresses errors like lexical entity divergence and logical directional inversion.
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The 'Indic Gap' in Performance
A consistent ~9.00% global performance drop is observed from English to Indic variants, with Telugu showing the maximum decline (~11.02%). This gap is primarily driven by increased schema-linking difficulty (20% of errors), greater structural ambiguity, and lack of external knowledge for Indic language queries.
9.00%
Average Performance Drop (English to Indic)
Impact of Evidence Augmentation
External evidence augmentation (SEED approach) consistently improves execution accuracy by +24% to +27% across all languages. This is particularly effective for Marathi (+27.5%), Tamil (+27.3%), and Telugu (+25.7%), where it helps bridge representational disparities between natural language and database schemas.
Evidence Augmentation Boosts Indic Language Performance
Consistent Accuracy Gains: DeepSeek V3.2 showed +24% to +27% execution accuracy improvement across all seven languages.
Maximal Impact in Indic: Marathi (+27.5%), Tamil (+27.3%), and Telugu (+25.7%) saw the highest gains.
Mechanism: Evidence files act as structural scaffolds, improving schema grounding and compositional reasoning by aligning natural language queries with canonical database values.
Strategic Implication: High efficacy in addressing representational disparities highlights the need for tailored solutions for linguistically diverse contexts.
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Implementation Roadmap
Our phased approach ensures a smooth and effective integration of advanced Text-to-SQL capabilities into your existing data infrastructure.
Phase 1: Discovery & Data Integration
Assess existing data landscapes, identify key databases, and integrate IndicDB-compatible schemas.
Phase 2: Model Adaptation & Fine-tuning
Tailor LLMs with evidence augmentation and specialized fine-tuning for optimal Indic language performance.
Phase 3: Deployment & User Enablement
Deploy Text-to-SQL solutions, provide training for non-expert users, and establish monitoring for continuous improvement.
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