A Comprehensive Benchmark of Language Models on Unicode and Romanized Sinhala

• URL: http://arxiv.org/abs/2601.14958v1
• Date: Wed, 21 Jan 2026 12:58:46 GMT
• Title: A Comprehensive Benchmark of Language Models on Unicode and Romanized Sinhala
• Authors: Minuri Rajapakse, Ruvan Weerasinghe,
• Abstract summary: This paper presents a benchmark of modern Language Models (LMs) on a diverse corpus of Unicode and Romanized Sinhala.<n>We evaluate open-source models using perplexity, a measure of how well a model predicts a text, and leading closed-source models.
• Score: 0.2864713389096699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The performance of Language Models (LMs) on lower-resource, morphologically rich languages like Sinhala remains under-explored, particularly for Romanized Sinhala, which is prevalent in digital communication. This paper presents a comprehensive benchmark of modern LMs on a diverse corpus of Unicode and Romanized Sinhala. We evaluate open-source models using perplexity, a measure of how well a model predicts a text, and leading closed-source models via a qualitative analysis of sentence completion. Our findings reveal that the Mistral-Nemo-Base-2407 model achieves the strongest predictive performance on Unicode text and the Mistral-7B-v0.3 model for Romanized text. The results also highlight the strong all-around performance of the Llama-3.1-8B model for both scripts. Furthermore, a significant performance disparity exists among closed-source models: Gemini-1.5-pro and DeepSeek excel at Unicode generation, whereas Claude-3.5-Sonnet is superior at handling Romanized text. These results provide an essential guide for practitioners selecting models for Sinhala-specific applications and highlight the critical role of training data in handling script variations.

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