Beyond Specialization: Benchmarking LLMs for Transliteration of Indian Languages
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912395252727808 |
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| author | Azam, Gulfarogh Sadique, Mohd Ali, Saif Nadeem, Mohammad Cambria, Erik Sohail, Shahab Saquib Alam, Mohammad Sultan |
| author_facet | Azam, Gulfarogh Sadique, Mohd Ali, Saif Nadeem, Mohammad Cambria, Erik Sohail, Shahab Saquib Alam, Mohammad Sultan |
| contents | Transliteration, the process of mapping text from one script to another, plays a crucial role in multilingual natural language processing, especially within linguistically diverse contexts such as India. Despite significant advancements through specialized models like IndicXlit, recent developments in large language models suggest a potential for general-purpose models to excel at this task without explicit task-specific training. The current work systematically evaluates the performance of prominent LLMs, including GPT-4o, GPT-4.5, GPT-4.1, Gemma-3-27B-it, and Mistral-Large against IndicXlit, a state-of-the-art transliteration model, across ten major Indian languages. Experiments utilized standard benchmarks, including Dakshina and Aksharantar datasets, with performance assessed via Top-1 Accuracy and Character Error Rate. Our findings reveal that while GPT family models generally outperform other LLMs and IndicXlit for most instances. Additionally, fine-tuning GPT-4o improves performance on specific languages notably. An extensive error analysis and robustness testing under noisy conditions further elucidate strengths of LLMs compared to specialized models, highlighting the efficacy of foundational models for a wide spectrum of specialized applications with minimal overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Specialization: Benchmarking LLMs for Transliteration of Indian Languages Azam, Gulfarogh Sadique, Mohd Ali, Saif Nadeem, Mohammad Cambria, Erik Sohail, Shahab Saquib Alam, Mohammad Sultan Computation and Language Artificial Intelligence Transliteration, the process of mapping text from one script to another, plays a crucial role in multilingual natural language processing, especially within linguistically diverse contexts such as India. Despite significant advancements through specialized models like IndicXlit, recent developments in large language models suggest a potential for general-purpose models to excel at this task without explicit task-specific training. The current work systematically evaluates the performance of prominent LLMs, including GPT-4o, GPT-4.5, GPT-4.1, Gemma-3-27B-it, and Mistral-Large against IndicXlit, a state-of-the-art transliteration model, across ten major Indian languages. Experiments utilized standard benchmarks, including Dakshina and Aksharantar datasets, with performance assessed via Top-1 Accuracy and Character Error Rate. Our findings reveal that while GPT family models generally outperform other LLMs and IndicXlit for most instances. Additionally, fine-tuning GPT-4o improves performance on specific languages notably. An extensive error analysis and robustness testing under noisy conditions further elucidate strengths of LLMs compared to specialized models, highlighting the efficacy of foundational models for a wide spectrum of specialized applications with minimal overhead. |
| title | Beyond Specialization: Benchmarking LLMs for Transliteration of Indian Languages |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.19851 |