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New IndicDBBench benchmark reveals LLM performance gap in Indian languages

By PulseAugur Editorial · [1 sources] ·

A new benchmark, IndicDBBench, has been developed to evaluate the Text-to-SQL capabilities of large language models (LLMs) in Indian languages. The benchmark, which includes 20 databases and over 15,000 tasks across seven languages, reveals a significant performance drop for LLMs when translating from English to Indic languages. This "Indic Gap" persists even when models are provided with gold tables and literal values, indicating a need for further development in cross-lingual semantic parsing for these languages. AI

IMPACT Highlights a critical performance gap in LLMs for Indian languages, necessitating further research and development for equitable AI capabilities.

RANK_REASON Research paper introducing a new benchmark for LLM evaluation. [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 IndicDBBench benchmark reveals LLM performance gap in Indian languages

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Research paper introducing a new benchmark for LLM evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Aviral Dawar, Roshan Karanth, Vikram Goyal, Rakhee Kulshrestha, Sumanta Pasari, Dhruv Kumar ·

    IndicDBBench - Benchmarking Multilingual Text-to-SQL Capabilities in Indian Languages

    arXiv:2604.13686v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have significantly advanced Text-to-SQL performance, existing benchmarks largely focus on Western contexts leaving a gap in real-world, non-Western applications. We introduce IndicDBBench, a co…

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