Authors: H. W. K. Aravinda, Rashad Sirajudeen, Samith Karunathilake, Nisansa de Silva, Surangika Ranathunga, Rishemjit Kaur
Organizations: Dept. of Computer Science & Engineering, University of Moratuwa, Sri Lanka. · School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand. · Central Scientific Instruments Organisation, Academy of Scientific and Innovative Research, Chandigarh, India. · IIT Ropar Technology & Innovation Foundation (iHub - AWaDH), India & CSIR-CSIO Chandigarh, India
Low-resource languages such as Sinhala are often overlooked by open-source Large Language Models (LLMs). In this research, we extend an existing multilingual LLM (Llama-3-8B) to better serve Sinhala. We enhance the LLM tokenizer with Sinhala specific vocabulary and perform continual pre-training on a cleaned 10 million 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.