@inproceedings{cahyawijaya-etal-2024-cendol,
title = "Cendol: Open Instruction-tuned Generative Large Language Models for {I}ndonesian Languages",
author = "Cahyawijaya, Samuel and
Lovenia, Holy and
Koto, Fajri and
Putri, Rifki and
Cenggoro, Wawan and
Lee, Jhonson and
Akbar, Salsabil and
Dave, Emmanuel and
Nuurshadieq, Nuurshadieq and
Mahendra, Muhammad and
Putri, Rr and
Wilie, Bryan and
Winata, Genta and
Aji, Alham and
Purwarianti, Ayu and
Fung, Pascale",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.796",
pages = "14899--14914",
abstract = "Large language models (LLMs) show remarkable human-like capability in various domains and languages. 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 {\textasciitilde}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.",
}
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%0 Conference Proceedings
%T Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages
%A Cahyawijaya, Samuel
%A Lovenia, Holy
%A Koto, Fajri
%A Putri, Rifki
%A Cenggoro, Wawan
%A Lee, Jhonson
%A Akbar, Salsabil
%A Dave, Emmanuel
%A Nuurshadieq, Nuurshadieq
%A Mahendra, Muhammad
%A Putri, Rr
%A Wilie, Bryan
%A Winata, Genta
%A Aji, Alham
%A Purwarianti, Ayu
%A Fung, Pascale
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F cahyawijaya-etal-2024-cendol
%X Large language models (LLMs) show remarkable human-like capability in various domains and languages. 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.
%U https://aclanthology.org/2024.acl-long.796
%P 14899-14914
Markdown (Informal)
[Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages](https://aclanthology.org/2024.acl-long.796) (Cahyawijaya et al., ACL 2024)
ACL
- Samuel Cahyawijaya, Holy Lovenia, Fajri Koto, Rifki Putri, Wawan Cenggoro, Jhonson Lee, Salsabil Akbar, Emmanuel Dave, Nuurshadieq Nuurshadieq, Muhammad Mahendra, Rr Putri, Bryan Wilie, Genta Winata, Alham Aji, Ayu Purwarianti, and Pascale Fung. 2024. Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14899–14914, Bangkok, Thailand. Association for Computational Linguistics.