Why Evaluating AI in Sinhala Is Harder Than in English
Low-resource languages like Sinhala present unique challenges for AI evaluation that are not present with high-resource languages like English. This post explores the specific obstacles: tokenization, data availability, benchmark quality, and evaluation methodology.
Low-Resource Languages
Sinhala has approximately 17 million speakers but limited digital presence compared to English. This scarcity affects every stage of AI development: model training, benchmark creation, and evaluation. Most open-weight models are trained on predominantly English data, resulting in poorer performance on Sinhala tasks.
Tokenization
Sinhala uses an abugida script where base characters combine with vowel modifiers. The tokenization challenge: many Sinhala words are morphologically complex and may be tokenized as single units in Sinhala-specific tokenizers but split across multiple English tokens. Example: the Sinhala word for "school" (විද්යාත්මක්) may be tokenized as one or multiple units depending on the tokenizer, affecting model attention and probability calculations.
Data Availability
Available Sinhala text corpora are small: the LankaCorpus contains approximately 500K words, while English corpora like Common Crawl contain billions. This data imbalance means models lack exposure to Sinhala grammar, idioms, and domain-specific vocabulary. Translation-based approaches (English-to-Sinhala) introduce errors and do not capture native nuances.
Benchmark Quality
Existing benchmarks for Sinhala are scarce and often adapted from English sources. The Sinhala MMLU subset, for example, has only 200 questions per category compared to 1,000+ for English. This small sample size increases variance in performance measurements and may not represent the full difficulty distribution.
Evaluation Challenges
- Unicode handling: Incorrect UTF-8 handling in preprocessing pipelines leads to garbled text and meaningless predictions.
- Domain coverage: Most evaluations focus on general knowledge; specialized domains (legal, medical, technical) have almost no Sinhala resources.
- Human reference quality: Reference answers in benchmarks may contain Sinhala script errors, making automatic metric computation (BLEU, ROUGE) unreliable.
Current evaluations often report inflated scores because metrics are not Sinhala-aware. A proper evaluation framework should include native Sinhala speakers for validation and domain-specific metrics that account for linguistic variation.
