  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 its capability to generalize to unseen tasks
and indigenous languages of Indonesia. Furthermore, Cendol models showcase
improved human favorability despite their limitations in capturing indigenous
knowledge and cultural values in Indonesia. In addition, we discuss the
shortcomings of parameter-efficient tunings, such as LoRA, for language
adaptation. Alternatively, we propose the usage of vocabulary adaptation to
enhance efficiency. Lastly, we evaluate the safety of Cendol and showcase that
safety in pre-training in one language such as English is transferable to
low-resource languages, such as Indonesian, even without RLHF and safety
fine-tuning.
