The Low-Resource Dilemma in Modern LLMs
While state-of-the-art models like GPT-4, Claude 3.5, and Gemini Pro can write coherent English essays, their performance in Sinhala reveals severe unevenness. Because Sinhala text represents a tiny fraction of total training tokens, models frequently produce hallucinations, garbled idioms, and inconsistent grammatical registers.
Why BLEU and chrF Fall Short for Sinhala
Automated evaluation metrics compare generated text against reference strings via n-gram overlap. However, Sinhala is an agglutinative language where affixes, case markers, and honorifics attach directly to noun and verb stems. A model can produce a semantically perfect sentence that receives an abysmal BLEU score simply because it used an alternative inflectional ending. Conversely, an n-gram overlap metric can award a high score to a hallucinated sentence that inverts the entire meaning.
True quality assessment requires human-in-the-loop evaluation frameworks.
True quality assessment requires human-in-the-loop evaluation frameworks.
The 4 Pillars of Sinhala LLM Evaluation
At Sinhalaize, we evaluate generative AI outputs across four core dimensions:
1. Factual Adequacy: Did the model follow the instructions, and are all stated claims factually grounded without hallucination?
2. Linguistic Fluency: Does the output adhere to proper Sinhala grammatical cases, or does it sound like a mechanical literal translation?
3. Register Consistency: Did the model maintain an appropriate formal or conversational tone throughout, without abrupt register shifts?
4. Cultural Safety & Alignment: Is the generated content respectful of Sri Lankan cultural sensibilities, free from offensive stereotypes or harmful content?
1. Factual Adequacy: Did the model follow the instructions, and are all stated claims factually grounded without hallucination?
2. Linguistic Fluency: Does the output adhere to proper Sinhala grammatical cases, or does it sound like a mechanical literal translation?
3. Register Consistency: Did the model maintain an appropriate formal or conversational tone throughout, without abrupt register shifts?
4. Cultural Safety & Alignment: Is the generated content respectful of Sri Lankan cultural sensibilities, free from offensive stereotypes or harmful content?
Frequently Asked Questions
What is RLHF in the context of Sinhala language models?
Reinforcement Learning from Human Feedback (RLHF) involves native Sinhala annotators reviewing multiple model completions for a single prompt and ranking them by helpfulness, accuracy, and safety. This preference data is then used to train reward models that align the AI.
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