Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysis
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909848127406080 |
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| author | Kamath, Anusha Singla, Kanishk Paul, Rakesh Joshi, Raviraj Vaidya, Utkarsh Chauhan, Sanjay Singh Wartikar, Niranjan |
| author_facet | Kamath, Anusha Singla, Kanishk Paul, Rakesh Joshi, Raviraj Vaidya, Utkarsh Chauhan, Sanjay Singh Wartikar, Niranjan |
| contents | Evaluating instruction-tuned Large Language Models (LLMs) in Hindi is challenging due to a lack of high-quality benchmarks, as direct translation of English datasets fails to capture crucial linguistic and cultural nuances. To address this, we introduce a suite of five Hindi LLM evaluation datasets: IFEval-Hi, MT-Bench-Hi, GSM8K-Hi, ChatRAG-Hi, and BFCL-Hi. These were created using a methodology that combines from-scratch human annotation with a translate-and-verify process. We leverage this suite to conduct an extensive benchmarking of open-source LLMs supporting Hindi, providing a detailed comparative analysis of their current capabilities. Our curation process also serves as a replicable methodology for developing benchmarks in other low-resource languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysis Kamath, Anusha Singla, Kanishk Paul, Rakesh Joshi, Raviraj Vaidya, Utkarsh Chauhan, Sanjay Singh Wartikar, Niranjan Computation and Language Machine Learning Evaluating instruction-tuned Large Language Models (LLMs) in Hindi is challenging due to a lack of high-quality benchmarks, as direct translation of English datasets fails to capture crucial linguistic and cultural nuances. To address this, we introduce a suite of five Hindi LLM evaluation datasets: IFEval-Hi, MT-Bench-Hi, GSM8K-Hi, ChatRAG-Hi, and BFCL-Hi. These were created using a methodology that combines from-scratch human annotation with a translate-and-verify process. We leverage this suite to conduct an extensive benchmarking of open-source LLMs supporting Hindi, providing a detailed comparative analysis of their current capabilities. Our curation process also serves as a replicable methodology for developing benchmarks in other low-resource languages. |
| title | Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysis |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2508.19831 |