Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
<|reference_start|>Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages: 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.<|reference_end|>
@article{cahyawijaya2024cendol:,
    title={Cendol: Open Instruction-tuned Generative Large Language Models for
  Indonesian Languages},
    author={Samuel Cahyawijaya, Holy Lovenia, Fajri Koto, Rifki Afina Putri,
  Emmanuel Dave, Jhonson Lee, Nuur Shadieq, Wawan Cenggoro, Salsabil Maulana
  Akbar, Muhammad Ihza Mahendra, Dea Annisayanti Putri, Bryan Wilie, Genta
  Indra Winata, Alham Fikri Aji, Ayu Purwarianti, Pascale Fung},
    journal={arXiv preprint arXiv:2404.06138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06138},
    primaryClass={cs.CL}
}