Aug 2025· Moratuwa Engineering Research Conference· pp. 617-622· 10 citations· ⚡ 1 influential· 48 references
Computer Science
TL;DR
This research extends an existing multilingual LLM (Llama-3-8B) to get a better coverage for Sinhala and enhances the LLM tokenizer with Sinhala specific vocabulary and performs continual pre-training on a 10 million sentence Sinhala corpus, resulting in the SinLlama model.
Abstract
Low-resource languages such as Sinhala are often overlooked by open-source Large Language Models (LLMs). Therefore, it is imperative that the existing LLMs are further trained to cover such languages. In this research, we extend an existing multilingual LLM (Llama-3-8B) to get a better coverage for Sinhala. We enhanced the LLM tokenizer with Sinhala specific vocabulary and performed continual pre-training on a 10 million sentence Sinhala corpus, resulting in the SinLlama model. This is the very first decoder-based open-source LLM with explicit Sinhala support. When SinLlama was instruction fine-tuned for three text classification tasks, it outperformed base and instruct variants of Llama-3-8B by a significant margin.
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