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English(EN) IndicDBBench - Benchmarking Multilingual Text-to-SQL Capabilities in Indian Languages

新的IndicDBBench基准揭示了大型语言模型在印度语言方面的性能差距

一个名为IndicDBBench的新基准已被开发出来,用于评估大型语言模型(LLMs)在印度语言中的文本到SQL能力。该基准包含20个数据库和跨越七种语言的15,000多个任务,揭示了当模型从英语翻译到印度语言时,LLMs的性能显著下降。这种“Indic Gap”即使在模型提供黄金表和字面值的情况下仍然存在,表明需要进一步开发这些语言的跨语言语义解析能力。 AI

影响 凸显了大型语言模型在印度语言方面存在的关键性能差距,亟需进一步的研究和开发以实现公平的AI能力。

排序理由 介绍LLM评估新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的IndicDBBench基准揭示了大型语言模型在印度语言方面的性能差距

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介绍LLM评估新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    IndicDBBench - 印度语言多语言文本到SQL能力基准测试

    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…