# Script Sensitivity: Benchmarking Language Models on Unicode, Romanized and Mixed-Script Sinhala

arXiv: https://arxiv.org/abs/2601.14958 · HTML: https://mlsanity.com/paper/2601.14958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Language Model Tokenizers Introduce Unfairness Between Languages](https://mlsanity.com/paper/2305.15425/) (2023)
- [Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World](https://mlsanity.com/paper/2210.08523/) (2022)
- [Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset](https://mlsanity.com/paper/2007.01176/) (2020)
- [The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks](https://mlsanity.com/paper/2210.10040/) (2022)
- [RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization](https://mlsanity.com/paper/2401.14280/) (2024)
- [SEA-HELM: Southeast Asian Holistic Evaluation of Language Models](https://mlsanity.com/paper/2502.14301/) (2025)
- [Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches](https://mlsanity.com/paper/2501.00529/) (2024)
- [SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala](https://mlsanity.com/paper/2509.03162/) (2025)
- [Script Gap: Evaluating LLM Triage on Indian Languages in Native vs Romanized Scripts in a Real World Setting](https://mlsanity.com/paper/2512.10780/) (2025)
- [SiTSE: Sinhala Text Simplification Dataset and Evaluation](https://mlsanity.com/paper/2412.01293/) (2024)
- [Improving Informally Romanized Language Identification](https://mlsanity.com/paper/2504.21540/) (2025)

## Abstract (arXiv)

The performance of Language Models (LMs) on low-resource, morphologically rich languages like Sinhala remains largely unexplored, particularly regarding script variation in digital communication. Sinhala exhibits script duality, with Unicode used in formal contexts and Romanized text dominating social media, while mixed-script usage is common in practice. This paper benchmarks 24 open-source LMs on Unicode, Romanized and mixed-script Sinhala using perplexity evaluation across diverse text sources. Results reveal substantial script sensitivity, with median performance degradation exceeding 300 times from Unicode to Romanized text. Critically, model size shows no correlation with script-handling competence, as smaller models often outperform architectures 28 times larger. Unicode performance strongly predicts mixed-script robustness but not Romanized capability, demonstrating that single-script evaluation substantially underestimates real-world deployment challenges. These findings establish baseline LM capabilities for Sinhala and provide practical guidance for model selection in multi-script low-resource environments.