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Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
April 10, 2024, 4:47 a.m. | Samuel Cahyawijaya, Holy Lovenia, Fajri Koto, Rifki Afina Putri, Emmanuel Dave, Jhonson Lee, Nuur Shadieq, Wawan Cenggoro, Salsabil Maulana Akbar, Muh
cs.CL updates on arXiv.org arxiv.org
Abstract: Large language models (LLMs) show remarkable human-like capability in various domains and languages. However, a notable quality gap arises in low-resource languages, e.g., Indonesian indigenous languages, rendering them ineffective and inefficient in such linguistic contexts. To bridge this quality gap, we introduce Cendol, a collection of Indonesian LLMs encompassing both decoder-only and encoder-decoder architectures across a range of model sizes. We highlight Cendol's effectiveness across a diverse array of tasks, attaining 20% improvement, and demonstrate …
abstract arxiv bridge capability collection cs.cl domains gap generative however human human-like instruction-tuned language language models languages large language large language models llms low quality rendering show them type
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