ntrex_128_eng-US_vie | ntrex_128_vie_eng-US | vie | Vietnam | 1997
ntrex_128_eng-US_hmv | ntrex_128_hmv_eng-US | hmv | Vietnam | 1997
nusax_mt_eng_ind | - | ind | Indonesia | 400
nusax_mt_eng_ace | nusax_mt_ace_eng | ace | Indonesia | 400
nusax_mt_eng_jav | nusax_mt_jav_eng | jav | Indonesia | 400
nusax_mt_eng_sun | nusax_mt_sun_eng | sun | Indonesia | 400
nusax_mt_eng_min | nusax_mt_min_eng | min | Indonesia | 400
nusax_mt_eng_bug | nusax_mt_bug_eng | bug | Indonesia | 400
nusax_mt_eng_bbc | nusax_mt_bbc_eng | bbc | Indonesia | 400
nusax_mt_eng_ban | nusax_mt_ban_eng | ban | Indonesia | 400
nusax_mt_eng_nij | nusax_mt_nij_eng | nij | Indonesia | 400
nusax_mt_eng_mad | nusax_mt_mad_eng | mad | Indonesia | 400
nusax_mt_eng_bjn | nusax_mt_bjn_eng | bjn | Indonesia | 400

Table 14: MT between English and SEA languages data subsets used in SEACrowd
NLG evaluation.

###  F.3 VL

  * •

Image-text (IMTEXT). This schema could be used for image captioning, text-to-
image generation, and vision-language pre-training. It consists of (id, text,
image_paths, metadata), where id denotes a unique row identifier of the
dataset, text denotes an input text, image_paths denotes a list of paths to
the input image sources, and metadata denotes relevant details such as visual
concepts and labels (if required).

  * •

General Image Classification (IMAGE). This schema could be used for image
classification both single-label and multi-label. It consists of (id, labels,
image_path, metadata), where id denotes a unique row identifier of the
dataset, labels denotes the label of that particular image (can be single-
label and multi-label), image_path denotes a list of paths to the input image
sources, and metadata denotes relevant details such as visual concepts and
labels (if required).

  * •

Image Question Answering (IMQA). This schema could be used for image/visual
question answering. It consists of (id, question_id, document_id, questions,
type, choices, context, answer, image_paths, meta), where id denotes a unique
row identifier of the dataset, question_id denotes a unique identifier of the
question, document_id denotes a unique identifier of the context document,
question denotes an input question to be answered, type denotes the type of
the QA task (e.g., extractive, multiple-choice, open-generative, closed-
generative, etc.), choices denotes a list of answer choices (if required),
context denotes a passage that serves as the background information of the
question (if required), and answer denotes the gold answer to the question (if
required), image_path denotes a list of paths to the input image sources, and
metadata denotes relevant details to allow some flexibility of the schema (if
required).

  * •

General Video-to-Text (VIDEO). This schema could be used for video-to-text
retrieval and video captioning. It consists of (id, video_path, text,
metadata), where id denotes a unique row identifier of the dataset, video_path
denotes the file path to an input video source, text denotes the text
associated with that particular frame/video, metadata denotes relevant details
such as the resolution, duration, and FPS of the video (if required).

##  Appendix G Supplementary Details for SEA Evaluation

Model | $\tau=0.01$ | $\tau=0.2$ | $\tau=0.5$ | $\tau=0.7$ | $\tau=1.0$
---|---|---|---|---|---
Commercial |  |  |  |  |
GPT-4 | 0.241 | 0.235 | 0.201 | 0.164 | 0.101
Command-R | 0.201 | 0.198 | 0.185 | 0.168 | 0.126
English |  |  |  |  |
Mistral | 0.161 | 0.160 | 0.159 | 0.162 | 0.150
Llama3 | 0.138 | 0.137 | 0.131 | 0.129 | 0.113
Falcon | 0.274 | 0.272 | 0.238 | 0.250 | 0.211
Multilingual |  |  |  |  |
mT0 | 0.151 | 0.148 | 0.131 | 0.112 | 0.074
BLOOMZ | 0.238 | 0.236 | 0.228 | 0.217 | 0.167
BactrianX-Llama | 0.163 | 0.162 | 0.163 | 0.168 | 0.149
AYA-23 | 0.183 | 0.182 | 0.183 | 0.179 | 0.135
AYA-101 | 0.112 | 0.109 | 0.095 | 0.085 | 0.069
SEA regional |  |  |  |  |
SEA-LION | 0.250 | 0.242 | 0.204 | 0.164 | 0.102
SeaLLM v2.5 | 0.137 | 0.133 | 0.116 | 0.097 | 0.069
Sailor | 0.152 | 0.151 | 0.145 | 0.139 | 0.113
SEA country |  |  |  |  |
Cendol-mT5 | 0.407 | 0.404 | 0.378 | 0.328 | 0.200
Cendol-Llama2 | 0.294 | 0.290 | 0.267 | 0.232 | 0.149
Merak v4 | 0.209 | 0.207 | 0.199 | 0.190 | 0.155
WangchanX-Llama3 | 0.163 | 0.161 | 0.153 | 0.150 | 0.131
Malaysian Llama3 | 0.181 | 0.181 | 0.179 | 0.176 | 0.143

Table 15: Language equity across baselines based on Gini coefficient weighted
by population with different $\tau$ values. Lower Gini means higher equity.

###  G.1 Datasets

Table 5, 6, 7, 8, and 9 provide the details of data subsets used in the NLU
evaluation. Sentiment analysis dataset is originally from NusaX Winata et al.
(2023), NusaTranslation Cahyawijaya et al. (2023b),
SentiTaglish202020https://huggingface.co/datasets/ccosme/SentiTaglishProductsAndServices,
SmSA Purwarianti and Crisdayanti (2019), PRDECT-ID Sutoyo et al. (2022), code-
mixed Indonesian-English sentiment Astuti et al. (2023), Karonese tweet
sentiment Karo et al. (2022), Typhoon Yolanda sentiment Imperial et al.
(2019), GKLMIP Khmer sentiment Jiang et al. (2022), Wisesight sentiment
corpus212121https://github.com/PyThaiNLP/wisesight-sentiment, Filipino-Tagalog
product reviews Sentiment222222https://github.com/EricEchemane/Filipino-
Tagalog-Product-Reviews-Sentiment-Analysis, and multilabel sentiment of
Indonesian mobile apps review Riccosan and Saputra (2023).

Topic classification dataset is originally from NusaParagraph Cahyawijaya et
al. (2023b), UIT-ViON Tran et al. (2021), SIB-200 Adelani et al. (2024),
GKLMIP Khmer news Jiang et al. (2022), and Indonesian news Muzad and Rahutomo
(2016). Natural Language Inference dataset is originally from IndoNLI Mahendra
et al. (2021), WreTe Setya and Mahendra (2018), SNLI Indo Putra et al. (2024),
MyXNLI232323https://huggingface.co/datasets/akhtet/myXNLI, and XNLI Conneau et
al. (2018). Commonsense reasoning dataset is originally from XStoryCloze Lin
et al. (2022), IndoCloze Koto et al. (2022), and EMoTES-3K Catapang and
Visperas (2023).

Open domain QA dataset is originally from IndoMMLU Koto et al. (2023b),
SeaEval Wang et al. (2023), M3Exam Zhang et al. (2023b), and Okapi Dac Lai et
al. (2023). Cultural QA dataset is originally from COPAL-ID Wibowo et al.
(2023), XCOPA Ponti et al. (2020), SeaEval Wang et al. (2023), and
Multilingual Fig-QA Kabra et al. (2023). The reading comprehension dataset is
originally from Belebele Bandarkar et al. (2023).

Table 10, 11, and 14 provide the details of data subsets used in the NLG
evaluation. The summarization dataset is originally from LR-Sum Palen-Michel
and Lignos (2023) and XL-Sum Hasan et al. (2021). The machine translation
dataset is originally from Lio and the Central Flores corpus Elias (2018),
Flores-200 Costa-jussà et al. (2024) and NTREX-128 Federmann et al. (2022).
Question answering dataset is originally from FacQA Purwarianti et al. (2007),
QASiNa Rizqullah et al. (2023), MKQA Longpre et al. (2021), and Open Thai
Wikipedia QA dataset242424https://zenodo.org/records/4539916.

Table 12 and 13 provide the details of data subsets used in the VL and speech
evaluation. The image captioning dataset is originally from XM3600 Thapliyal
et al. (2022). Speech recognition dataset is originally from INDspeech NEWSTRA
Ethnic collection Sani et al. (2012), ASR Iban Juan et al. (2015), FLEURS
Conneau et al. (2022), and Common Voice Ardila et al. (2020).

###  G.2 Baselines

Table 19, 20, and 21 report the details of baseline models used in SEACrowd
evaluation (§3). For each baseline model, we provide information regarding the
model size, origin base model, seen languages in the training corpora use, and
the URL where the models can be downloaded. In principle, this work does not
aim to acquire and fit all available SEA-trained LLMs over the Internet, as
this is computationally expensive. Rather, we want to initiate the exploration
of select publicly available models to serve as baselines for the evaluation
of foundational capabilities on SEA languages through benchmarking on NLU,
NLG, speech, and vision tasks aggregated via SEACrowd.

Across the various models explored, as listed in the tables, we prioritized
the diversity of model variation in terms of scale, openness, and coverage of
SEA languages. In NLP tasks, we covered five language model families for the
main experiments, namely English-only, multilingual, regional, and country-
specific models. Instruction-tuned LLMs demonstrate the ability to generalize
to unseen tasks Wei et al. (2021); Sanh et al. (2021); Ouyang et al. (2022).
When these LLMs are based on a multilingual foundation, they have shown
proficiency in generalizing across multiple languages Muennighoff et al.
(2022); Adilazuarda et al. (2023); Zhang et al. (2023a). For NLU, we compute
the weighted F1-score and obtain the answers via log-likelihood for open-
source baselines or string matching for commercial baselines.

For the speech benchmark, only two model families are available: multilingual
models and models fine-tuned on specific SEA languages. For vision tasks, we
covered English-only and one multilingual model. These models utilize a visual
backbone pre-trained on image-text alignment, e.g., CLIP Radford et al.
(2021), to project image features into the input space of an existing pre-
trained LM. In summary, we mostly explored open models readily accessible on
HuggingFace but also included commercial models such as GPT-4 and Whisper V3
for performance benchmarking, reproducibility, and extension by future works.

Model | Hyperparameter | Value
---|---|---
Logistic Regression | max_iter | 100
| C | np.linspace(0.001, 10, 100)
Naive Bayes | alpha | np.linspace(0.001, 1, 50)
| distribution | MultinomialNB
SVM | C | 1
| kernel | ["rbf", "linear"]

Table 16: Hyper-parameters of classical models for Translationese prediction
through grid search.

###  G.3 Prompts

Tables 22, 23, and 24 describe the handwritten prompt templates used in NLU,
NLG, and VL evaluation (§3). For all tasks, we used a zero-shot prompting
procedure to serve as the baseline setup. Due to the task complexity and
distribution of workload from volunteer contributors with available computing
resources, we limited the experiment procedure for some setups to ensure the
acquisition of results in line with target release dates. For NLU, we explored
three prompt styles for each dataset from core tasks, including commonsense
reasoning, question-answering, and NLI. For more challenging tasks requiring
more intensive computing power such as NLG and VL, we used only one uniform
prompt style, but we also explored prompts translated into SEA languages,
i.e., Filipino, Indonesian, Thai, and Vietnamese for VL.

Model | 3-label | HT vs. MT-Nat | MT vs. HT-Nat | Nat vs. HT-MT
---|---|---|---|---
LR (TF-IDF) | 39.73 | 53.03 | 56.01 | 75.20
LR (BoW) | 45.63 | 55.90 | 61.39 | 75.60
NB (TF-IDF) | 33.43 | 49.53 | 50.55 | 73.05
NB (BoW) | 33.70 | 49.10 | 50.64 | 71.26
SVM (TF-IDF) | 39.55 | 52.63 | 55.10 | 76.40
SVM (BoW) | 46.84 | 56.85 | 61.40 | 75.65
mDeBERTa | 51.51 | 64.77 | 59.16 | 79.08

Table 17: Results of translationese classifier (accuracy) averaged across
languages.

###  G.4 Evaluation Results

Table 25 and 26 describes the NLU and NLG results per language.

###  G.5 Language Equity Results

Table 15 presents the language equity of LLMs used in the evaluation across
different weights of the number of language speakers in the Gini coefficient
calculation.

##  Appendix H Supplementary Details for Translationese Classifier

###  H.1 Training & Evaluation Data

We manually select and validate the text collection method of each data subset
for training and evaluating the translationese classifier, in Tables 27 and
28, respectively. This validation is done by checking the relevant
publication, domain, and annotation method. If the texts in the data subsets
are a product of machine or human translation, we regard them as
translationese. We label data subsets with human-generated texts as natural
data.

###  H.2 Experiments

We aim to assess the capability of ML models to differentiate between human-
generated/natural samples (Nat), human-translated samples (HT), and machine-
translated samples (MT). Our approach involves training classifiers using
classical ML techniques and fine-tuning mDeBERTa models to enhance learning.
Furthermore, we experiment by combining two label classes into one to evaluate
the predictive difficulty of distinguishing between these labels. This
analysis provides valuable insights into the relative similarity of the
samples across these categories. The following section provides a
comprehensive overview of our methodology for this study.

#### Classical ML

We use three classical machine learning methods: 1) Logistic Regression (LR),
2) Naive Bayes (NB), and 3) Support Vector Machine (SVM) with two different
features, including TF-IDF and Bag-of-words (BoW). We run hyper-parameter
tuning with grid search to find the best hyper-parameters for each method on
validation set, and report the results on test set in Table 16.

#### Encoder LM

We explore fine-tuning encoder-only LM for developing a translationese
classifier. We utilize mDeBERTa-v3base
model252525https://huggingface.co/microsoft/mdeberta-v3-base He et al. (2020,
2022) – a multilingual encoder-only LM – as our backbone. We trained the model
with AdamW Loshchilov and Hutter (2019) optimizer using a learning rate of
1e-5, batch size of 256, and warming up steps of 500 for a maximum of 10
epochs. We apply an early stopping of 3 epochs based on the validation
accuracy.

Country | Affiliation | Origin
---|---|---
Indonesia | 16 | 31
Malaysia | 0 | 1
Philippines | 3 | 7
Singapore | 13 | 2
Thailand | 1 | 2
Vietnam | 0 | 1
Australia | 1 | 0
Brazil/Sweden | 0 | 1
Canada | 1 | 0
China | 2 | 8
Egypt | 0 | 1
Germany | 0 | 2
Hong Kong | 2 | 0
India | 0 | 1
Ireland | 1 | 0
Japan | 3 | 0
The Netherlands | 0 | 1
UAE | 5 | 0
UK | 4 | 0
USA | 9 | 1
Uzbekistan | 0 | 2

Table 18: The demographics of the authors based on affiliation country and
origin country.

##  Appendix I Supplementary Details for SEA Language Prioritization

Based on the results of the global utility metric Blasi et al. (2022), we
provide the top-20 SEA indigenous languages to be prioritized based on their
demand (i.e., the number of SEA language speakers) and current utility (Figure
10) or resource availability (Figure
11).262626https://github.com/SEACrowd/globalutility While the current utility,
also known as the model capability, is relative to the model performance on
eng, the resource availability is relative to 500, which is approximately the
number of datasets in Korean language available in HuggingFace. The Korean
language is chosen as the pivot because it is considered a higher-resource
language than most by Joshi et al. (2020).

##  Appendix J Contributor Demographics

Table 18 describes the geographical distribution of the authors in SEACrowd.

##  Appendix K Languages Under Study

Table 29-47 present the list of SEA indigenous languages covered by SEACrowd.
Information regarding the ISO 639-3 code, language name, region, and
population is obtained from Eberhard et al. (2021); Hammarström et al. (2024);
Project (2024); Dryer and Haspelmath (2013) and
Wikipedia272727https://www.wikipedia.org/.

Model name | Model size | Backbone | Seen langs |  URL
---|---|---|---|---
Commercial
GPT-4 | N/A | GPT-4 | N/A |  https://openai.com/index/gpt-4/
Command-R | 36B | Command-R | 2 SEA langs (vie, ind), 22 non-SEA langs |  https://cohere.com/blog/command-r
English
Mistral | 7B | Mistral | N/A |  mistralai/Mistral-7B-Instruct-v0.3
Llama3 | 8B | Llama3 | N/A |  meta-llama/Meta-Llama-3-8B-Instruct
Falcon | 7B | Falcon | 0 SEA langs (mainly English) |  tiiuae/falcon-7b-instruct
Multilingual
mT0 | 3B | mT5 | 2 SEA langs (vie, ind), 43 non-SEA langs |  bigscience/mt0-xl
BLOOMZ | 7B | BLOOM | 2 SEA langs (vie, ind), 43 non-SEA langs |  bigscience/bloomz-3b
BactrianX-Llama | 7B | Llama | 6 SEA langs (ind, vie, khm, mya, tha, tgl, vie), 46 non-SEA langs |  MBZUAI/bactrian-x-llama-7b-merged
AYA-23 | 8B | Command | 2 SEA langs (ind, vie), 21 non-SEA langs |  CohereForAI/aya-23-8B
AYA-101 | 13B | T5 | 9 SEA langs (ind, vie, tha, zsm, mya, ceb, fil, jav, sun), 92 non-SEA langs |  CohereForAI/aya-101
SEA regional
SEA-LION | 7B | MPT | 8 SEA langs (ind, vie, tha, tgl, zsm, khm, lao, mya), 3 non-SEA langs |  aisingapore/sea-lion-7b-instruct
SeaLLM v2.5 | 7B | SeaLLM | 8 SEA langs (ind, vie, tha, tgl, zsm, khm, lao, mya) |  SeaLLMs/SeaLLM-7B-v2.5
Sailor | 7B | Qwen 1.5 | 5 SEA langs (ind, vie, lao, zlm, tha), 2 non-SEA langs |  sail/Sailor-7B-Chat
SEA country
Cendol-mT5 | 3B | mT5 | 1 SEA lang (ind), 18 local Indonesian langs |  indonlp/cendol-mt5-xl
Cendol-Llama2 | 7B | Llama2 | 1 SEA lang (ind), 18 local Indonesian langs |  indonlp/cendol-llama2-7b
Merak v4 | 7B | Llama2 | 1 SEA lang (ind) |  Ichsan2895/Merak-7B-v4
WangchanX-Llama3 | 8B | Llama3 | 4 SEA langs (ind, vie, tha, mya) and 26 non-SEA langs |  airesearch/LLaMa3-8b-WangchanX-sft-Demo
Malaysian Llama3 | 8B | Llama3 | 1 SEA lang (zlm) |  mesolitica/malaysian-llama-3-8b-instruct-16k

Table 19: LLMs used in SEACrowd NLU and NLG evaluation.

Model name | Model size | Backbone | Seen langs |  URL
---|---|---|---|---
Multilingual
Whisper v3 | 1.54B | Whisper v3 | 89 non-SEA & 9 SEA (ind, jav, lao, zlm, mya, tgl, tha, sun, vie) |  openai/whisper-large-v3
MMS 1B | 1B | MMS |  993 non-SEA & 205 SEA (abp, ace, acn, agn, ahk, akb, alj, alp, amk, aoz, atb, atq, ayz, ban, bbc, bcl, bdg, bdq, bep, bgr, bhz, bkd, blt, blx, blz, bno, bpr, bps, bru, btd, bts, btx, bvz, bzi, ceb, cek, cfm, cgc, cmr, cnh, ctd, dbj, dnt, dnw, dtp, eip, frd, gbi, gor, had, hap, hil, hlt, hnn, hvn, iba, ifa, ifb, ifk, ifu, ify, ilo, ind, itv, jav, jmd, kac, kak, kdt, khg, khm, kje, kjg, klw, kmd, kml, knb, kne, kpq, kps, kqe, kqr, krj, krr, kvw, kxf, kxm, kyb, kyo, kyu, kzf, lao, law, lbw, lcp, lew, lex, lhu, lis, lje, ljp, llg, lnd, lsi, mad, mak, mbb, mbt, mej, mhx, mhy, min, mkn, mnb, mnw, mnx, mog, mqf, mqj, mqn, mrw, mtd, mtj, mvp, mwq, mwv, mya, myl, nfa, nia, nij, nlc, nlk, nod, npy, nst, obo, pag, pam, pce, pez, plw, pmf, ppk, prf, prk, prt, pse, ptu, pww, raw, rej, rgu, rhg, ril, rol, saj, sas, sbl, sda, sea, sgb, shn, sjm, slu, sml, sne, suc, sun, sxn, sya, sza, tbk, tbl, tby, tcz, tdj, tes, tgl, tha, tih, tlb, tnt, tom, tvw, twb, twe, twu, txa, txq, ubl, urk, ury, vie, war, wlo, xdy, xmm, xsb, xte, yka, yli, yva, zlm, zyp) |  facebook/mms-1b-all
Seamless M4T v2 | 2.3B | Seamless | 83 non-SEA & 9 SEA (ind, jav, khm, lao, mya, tgl, tha, vie, zlm) |  facebook/seamless-m4t-v2-large
Fine-tuned on specific language(s)
XLSR English | 300M | Wav2Vec2 |  46 non-SEA & 7 SEA (ceb, cnh, ind, lao, tam, tgl, vie) & fine-tuning language(s) |  jonatasgrosman/wav2vec2-large-xlsr-53-english
XLSR Ind-Jav-Sun |  indonesian-nlp/wav2vec2-indonesian-javanese-sundanese
XLSR Indonesian |  Galuh/wav2vec2-large-xlsr-indonesian
XLSR Thai |  wannaphong/wav2vec2-large-xlsr-53-th-cv8-newmm
XLS-R Tagalog |  sil-ai/wav2vec2-bloom-speech-tgl
XLS-R Burmese |  sil-ai/wav2vec2-bloom-speech-mya
XLS-R Khmer |  vitouphy/wav2vec2-xls-r-300m-khmer
Whisper Indonesian | 1.54B | Whisper | 89 non-SEA & 9 SEA (ind, jav, lao, msa, mya, tgl, tha, sun, vie) |  cahya/whisper-large-id
Whisper Thai |  biodatlab/whisper-th-large-v3-combined
Whisper Khmer |  ksoky/whisper-large-khmer-asr

Table 20: Speech models used in SEACrowd speech evaluation.

Model name | Model size | Backbone | Pre-training images |  URL
---|---|---|---|---
English
LLaVA 1.5 | N/A | N/A | N/A |  N/A
LLaVA 1.6 | 7B | Mistral-7B | N/A |  liuhaotian/llava-v1.6-mistral-7b
Idefics2 | 8B | Mistral-7B-v0.1 | 1.5B |  HuggingFaceM4/idefics2-8b
PaliGemma | 2B | Gemma-2B | N/A |  google/paligemma-3b-pt-224
Multilingual
mBLIP | N/A | blip2-flan-t5-xl | N/A |  Gregor/mblip-mt0-xl

Table 21: VLMs used in SEACrowd VL evaluation.

No. |  Prompt template
---|---
Sentiment Analysis
1 |  Classify the sentiment of the text below.\n[INPUT] => Sentiment ([OPTIONS]): [LABEL_CHOICE]
2 |  Predict the sentiment of the following text.\nText: [INPUT]\nAnswer with [OPTIONS]: [LABEL_CHOICE]
3 |  [INPUT]\nWhat would be the sentiment of the text above? [OPTIONS]? [LABEL_CHOICE]
Topic Classification
1 |  Classify the topic of the text below.\n[INPUT] => Topic ([OPTIONS]): [LABEL_CHOICE]
2 |  Predict the topic of the following text.\nText: [INPUT]\nAnswer with [OPTIONS]: [LABEL_CHOICE]
3 |  [INPUT]\nWhat would be the topic of the text above? [OPTIONS]? [LABEL_CHOICE]
Commonsense Reasoning $\rightarrow$ *_seacrowd_text
1 |  Classify the morality of the text below.\n[INPUT] => Morality ([OPTIONS]): [LABEL_CHOICE]
2 |  Predict the morality of the following text.\nText: [INPUT]\nAnswer with [OPTIONS]: [LABEL_CHOICE]
3 |  [INPUT]\nWhat would be the morality of the text above? [OPTIONS]? [LABEL_CHOICE]
Commonsense Reasoning $\rightarrow$ *_seacrowd_qa
1 |  Question: [QUESTION]\nWhat reply makes more sense to answer this question?\nChoices: [ANSWER_CHOICES]\nAnswer: [LABEL_CHOICE]
2 |  Based on the the following question: "[QUESTION]" and choices: [ANSWER_CHOICE the correct answer is: [LABEL_CHOICE]
3 |  Question: [QUESTION]\nChoices: [ANSWER_CHOICES]\nThe correct answer to the given question is: [LABEL_CHOICE]
All QAs
1 |  Refer to the passage below and answer the following question:\nPassage: [CONTEXT]\nQuestion: [QUESTION]\nChoices: [ANSWER_CHOICES]\nAnswer: [LABEL_CHOICE]
2 |  [CONTEXT]\nBased on the above text, [QUESTION]\nChoices: [ANSWER_CHOICES]\nAnswer: [LABEL_CHOICE]
3 |  [CONTEXT]\nQuestion: [QUESTION]\nChoices:[ANSWER_ CHOICES]\nReferring to the passage above, the correct answer to the given question is: [LABEL_CHOICE]
NLI
1 |  Hypothesis: [INPUT_A]\nPremise: [INPUT_B]\nQuestion: What is the relation between the hypothesis and the premise? [OPTIONS]? [LABEL_CHOICE]
2 |  Given the following premise and hypothesis:\nHypothesis: [INPUT_A]\nPremise: [INPUT_B]\nDetermine the logical relationship (([OPTIONS])): [LABEL_CHOICE]
3 |  Choose the most appropriate relationship ([OPTIONS]) between the premise and hypothesis:\nRelationship between "[INPUT_B]" and "[INPUT_A]": [LABEL_CHOICE]

Table 22: Prompt templates used for NLU tasks.

No. |  Prompt template
---|---
Machine Translation (MT)
1 |  Translate the following text from [SOURCE] to [TARGET]. Give your translation directly.\nText: [INPUT]\nTranslation:
Summarization
1 |  Write a summary from the following text.\nText: [INPUT]\nSummary:
Abstractive & Extractive QA
1 |  Refer to the passage below and answer the following question:\nPassage: [CONTEXT]\nQuestion: [QUESTION]\nAnswer:

Table 23: Prompt templates used for NLG tasks.

Lang. |  Prompt template
---|---
Image Captioning
eng |  Caption the following image in [LANGUAGE].
fil |  Ilarawan ang sumusunod na larawan.
ind |  Deskripsikan gambar berikut.

Table 24: Prompt templates used for the image captioning task in VL
evaluation.

| abl | abs | ace | ban | bbc | bew | bhp | bjn | btx | bug | ceb | eng | fil | ilo | ind | jav | kac | khm | lao | lus | mad | mak | min | mui | mya | nij | pag | rej | shn | sun | tha | vie | war | zsm | Overall
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
GPT-4 | 63.3 | 39.0 | 39.3 | 60.3 | 7.1 | 68.5 | 2.8 | 60.4 | 27.8 | 40.4 | 85.6 | 52.1 | 55.9 | 69.5 | 60.7 | 59.7 | 30.8 | 66.4 | 51.8 | 70.0 | 37.1 | 44.3 | 57.9 | 71.8 | 47.6 | 40.2 | 79.4 | 34.0 | 21.7 | 58.5 | 59.6 | 56.1 | 84.9 | 61.6 | 51.9
Command-R | 50.1 | 80.8 | 57.6 | 62.8 | 47.4 | 81.8 | 58.2 | 57.1 | 57.3 | 57.9 | 66.7 | 69.4 | 51.1 | 56.8 | 58.3 | 61.2 | 36.5 | 41.5 | 33.8 | 63.9 | 61.9 | 58.4 | 66.4 | 81.7 | 34.8 | 53.3 | 75.6 | 69.6 | 35.4 | 63.2 | 42.7 | 55.9 | 67.6 | 55.7 | 58.0
Mistral | 36.7 | 53.6 | 46.4 | 49.6 | 33.0 | 59.3 | 44.3 | 44.6 | 44.3 | 48.8 | 53.5 | 69.2 | 48.4 | 49.1 | 52.5 | 46.7 | 33.2 | 29.8 | 30.7 | 56.1 | 45.7 | 44.8 | 51.2 | 62.6 | 27.4 | 40.1 | 69.2 | 48.6 | 31.9 | 48.3 | 40.8 | 45.2 | 54.4 | 49.6 | 46.8
Llama3 | 37.3 | 40.3 | 43.2 | 48.9 | 34.8 | 44.5 | 32.6 | 42.2 | 38.5 | 42.9 | 51.2 | 59.5 | 45.2 | 46.7 | 49.2 | 44.4 | 28.5 | 34.6 | 30.3 | 46.8 | 39.0 | 38.0 | 43.6 | 49.2 | 35.2 | 39.6 | 60.5 | 38.5 | 31.1 | 45.2 | 43.8 | 45.5 | 50.3 | 49.0 | 42.6
Falcon | 21.1 | 63.2 | 13.3 | 19.0 | 23.0 | 37.9 | 62.1 | 15.6 | 31.9 | 15.7 | 19.5 | 43.7 | 25.1 | 18.8 | 30.8 | 27.0 | 14.2 | 10.2 | 12.7 | 15.0 | 30.3 | 32.3 | 23.6 | 37.0 | 18.0 | 23.0 | 18.8 | 36.0 | 14.1 | 28.2 | 15.9 | 18.8 | 19.1 | 17.4 | 25.1
mT0 | 37.6 | 63.6 | 43.7 | 51.2 | 37.0 | 66.1 | 38.4 | 43.6 | 41.3 | 50.3 | 62.5 | 49.4 | 41.0 | 59.0 | 47.2 | 56.0 | 40.9 | 57.5 | 61.2 | 57.0 | 46.7 | 45.8 | 52.6 | 68.8 | 45.9 | 40.9 | 62.6 | 47.8 | 47.0 | 58.8 | 41.8 | 41.4 | 61.4 | 49.4 | 50.5
BLOOMZ | 25.6 | 66.5 | 28.4 | 34.2 | 35.8 | 53.9 | 48.0 | 30.4 | 36.3 | 33.3 | 30.9 | 51.7 | 28.9 | 27.8 | 44.7 | 38.2 | 23.1 | 18.9 | 23.6 | 28.1 | 37.8 | 34.5 | 39.9 | 60.2 | 23.0 | 34.6 | 33.1 | 42.2 | 19.8 | 41.3 | 25.9 | 34.8 | 32.1 | 34.3 | 35.3
BactrianX-Llama | 24.9 | 48.6 | 21.2 | 28.5 | 26.9 | 33.4 | 45.9 | 22.8 | 31.4 | 22.7 | 27.9 | 45.6 | 32.0 | 24.3 | 38.3 | 30.0 | 19.9 | 17.0 | 20.7 | 21.0 | 30.0 | 28.8 | 26.2 | 35.7 | 22.8 | 27.2 | 26.5 | 29.2 | 20.5 | 30.2 | 24.5 | 27.1 | 28.3 | 31.5 | 28.6
AYA-23 | 43.3 | 21.2 | 26.9 | 35.0 | 24.3 | 31.2 | 16.8 | 30.9 | 25.1 | 26.5 | 36.0 | 50.8 | 33.5 | 32.7 | 46.8 | 36.9 | 20.5 | 15.1 | 22.0 | 27.4 | 31.0 | 31.7 | 27.3 | 35.5 | 23.7 | 37.3 | 32.6 | 22.8 | 20.8 | 34.9 | 32.7 | 44.8 | 37.1 | 47.9 | 31.3
AYA-101 | 42.5 | 64.3 | 71.2 | 65.2 | 58.8 | 68.2 | 43.3 | 63.5 | 52.7 | 60.7 | 71.7 | 62.8 | 52.8 | 65.0 | 54.2 | 62.6 | 43.1 | 62.2 | 67.8 | 71.8 | 56.9 | 49.0 | 69.3 | 70.2 | 51.5 | 57.2 | 75.7 | 52.9 | 53.8 | 67.2 | 49.5 | 48.0 | 70.5 | 56.4 | 59.8
SEA-LION | 10.3 | 62.3 | 13.5 | 16.5 | 21.3 | 35.3 | 60.3 | 13.4 | 31.8 | 15.2 | 13.6 | 26.6 | 20.6 | 10.2 | 27.6 | 21.4 | 8.7 | 16.8 | 15.2 | 12.5 | 26.8 | 28.3 | 22.8 | 34.6 | 23.0 | 16.0 | 14.4 | 34.1 | 9.7 | 23.4 | 16.3 | 14.7 | 14.2 | 13.3 | 21.9
SeaLLM v2.5 | 50.7 | 55.1 | 34.5 | 43.4 | 36.3 | 53.9 | 53.2 | 45.8 | 45.8 | 37.7 | 47.6 | 42.5 | 52.6 | 44.7 | 53.4 | 49.8 | 27.4 | 42.6 | 50.3 | 45.8 | 48.7 | 49.8 | 46.8 | 58.4 | 41.0 | 39.1 | 55.7 | 47.8 | 28.7 | 50.1 | 49.0 | 54.5 | 55.4 | 60.6 | 47.0
Sailor | 50.4 | 59.2 | 43.8 | 55.5 | 44.1 | 61.5 | 43.9 | 50.5 | 44.8 | 45.7 | 45.6 | 63.0 | 40.2 | 45.0 | 51.3 | 53.1 | 29.9 | 32.7 | 53.9 | 53.9 | 47.6 | 46.5 | 52.8 | 63.9 | 28.1 | 52.7 | 59.3 | 42.2 | 26.7 | 54.0 | 46.3 | 47.7 | 49.2 | 52.1 | 48.1
Cendol-mT5 | 15.0 | 98.5 | 38.3 | 42.3 | 84.7 | 99.4 | 95.6 | 33.3 | 92.6 | 68.6 | 14.1 | 38.7 | 23.8 | 12.2 | 33.4 | 50.5 | 10.4 | 20.3 | 15.3 | 9.6 | 76.5 | 70.2 | 65.2 | 99.6 | 16.6 | 52.6 | 12.8 | 98.9 | 7.2 | 56.6 | 26.4 | 14.7 | 15.1 | 15.9 | 44.8
Cendol-Llama2 | 17.5 | 80.0 | 30.8 | 33.5 | 60.6 | 49.3 | 73.4 | 27.9 | 45.1 | 32.3 | 18.7 | 36.8 | 21.4 | 17.8 | 37.4 | 35.1 | 14.7 | 13.2 | 15.9 | 15.0 | 46.3 | 38.1 | 37.1 | 51.6 | 19.9 | 40.3 | 17.7 | 47.7 | 16.5 | 38.5 | 20.6 | 17.3 | 18.5 | 18.4 | 32.5
Merak | 37.0 | 68.6 | 37.7 | 48.3 | 36.4 | 66.1 | 60.1 | 41.4 | 50.4 | 47.8 | 42.4 | 59.6 | 37.9 | 39.7 | 48.5 | 48.4 | 27.9 | 24.2 | 28.0 | 44.3 | 51.7 | 51.0 | 50.5 | 70.3 | 27.2 | 40.0 | 58.6 | 57.9 | 28.6 | 50.8 | 29.3 | 35.3 | 43.7 | 47.1 | 45.2
WangchanX-Llama3 | 38.4 | 59.3 | 26.8 | 35.2 | 35.0 | 43.3 | 56.9 | 31.6 | 38.3 | 31.2 | 32.3 | 57.6 | 36.6 | 29.3 | 45.0 | 38.7 | 23.7 | 24.3 | 25.1 | 26.6 | 40.4 | 41.4 | 34.8 | 43.6 | 31.6 | 37.0 | 31.2 | 42.9 | 23.5 | 39.8 | 36.5 | 38.4 | 31.3 | 37.0 | 36.6
Malaysian Llama3 | 38.9 | 62.3 | 38.1 | 41.9 | 39.2 | 46.9 | 58.3 | 39.5 | 40.5 | 35.9 | 37.8 | 55.5 | 34.5 | 33.1 | 48.6 | 42.6 | 24.7 | 18.9 | 20.4 | 33.6 | 42.1 | 41.0 | 42.5 | 48.5 | 22.2 | 39.6 | 46.8 | 41.1 | 19.6 | 44.0 | 33.7 | 34.6 | 37.7 | 49.9 | 39.2
Overall | 35.6 | 60.4 | 36.4 | 42.9 | 38.1 | 55.6 | 49.7 | 38.6 | 43.1 | 39.7 | 42.1 | 51.9 | 37.9 | 37.9 | 46.0 | 44.6 | 25.5 | 30.3 | 32.1 | 38.8 | 44.3 | 43.0 | 45.0 | 58.0 | 30.0 | 39.5 | 46.1 | 46.4 | 25.4 | 46.3 | 35.3 | 37.5 | 42.8 | 41.5 | 41.4

Table 25: NLU evaluation results in weighted F1-score per language.

| ace | ban | bbc | bjn | bug | ceb | fil | hmv | ilo | ind | jav | kac | khm | lao | ljl | lus | mad | min | mya | nij | pag | shn | sun | tha | vie | war | zsm | Overall
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
GPT-4 | 5.8 | 6.0 | 7.4 | 4.7 | 5.6 | 13.7 | 9.5 | 8.5 | 14.2 | 3.7 | 6.8 | 7.4 | 2.7 | 3.4 | 2.7 | 11.3 | 3.7 | 6.3 | 2.8 | 4.2 | 10.4 | 3.0 | 6.1 | 2.1 | 10.0 | 13.2 | 5.0 | 6.7
Command-R | 19.6 | 26.1 | 16.4 | 30.0 | 16.0 | 44.3 | 52.5 | 16.8 | 29.4 | 57.9 | 32.6 | 8.8 | 8.7 | 14.2 | 6.0 | 19.5 | 17.2 | 31.6 | 9.5 | 18.4 | 20.4 | 8.9 | 27.5 | 24.3 | 46.8 | 34.4 | 50.1 | 25.5
Mistral | 12.4 | 15.0 | 10.0 | 13.9 | 11.1 | 28.5 | 37.2 | 10.2 | 15.9 | 28.6 | 15.4 | 7.3 | 8.7 | 10.8 | 4.2 | 11.7 | 9.5 | 18.0 | 5.7 | 12.4 | 17.5 | 9.5 | 14.8 | 15.1 | 25.1 | 22.4 | 31.1 | 15.6
Llama3 | 11.0 | 12.3 | 8.1 | 13.8 | 7.6 | 25.1 | 33.2 | 7.6 | 18.4 | 21.9 | 17.0 | 4.8 | 6.5 | 5.8 | 3.2 | 9.6 | 8.5 | 16.4 | 4.5 | 9.5 | 11.8 | 6.3 | 15.1 | 9.6 | 21.7 | 20.5 | 25.2 | 13.2
Falcon | 7.3 | 9.5 | 8.2 | 8.3 | 7.9 | 18.6 | 23.6 | 6.6 | 9.7 | 15.3 | 7.7 | 6.0 | 3.1 | 3.1 | 4.2 | 9.3 | 6.6 | 11.8 | 1.8 | 8.7 | 12.9 | 4.5 | 7.7 | 2.4 | 13.5 | 13.5 | 17.0 | 9.2
mT0 | 4.8 | 5.6 | 3.7 | 5.7 | 3.1 | 4.6 | 6.8 | 4.5 | 3.8 | 29.3 | 5.8 | 2.1 | 4.3 | 6.1 | 1.7 | 3.4 | 3.6 | 6.5 | 5.0 | 3.5 | 3.6 | 3.5 | 6.8 | 9.4 | 19.6 | 6.1 | 9.1 | 6.4
BLOOMZ | 3.8 | 4.6 | 2.8 | 5.3 | 2.9 | 4.1 | 5.1 | 3.4 | 4.2 | 32.3 | 4.9 | 3.0 | 1.5 | 2.4 | 1.5 | 4.0 | 2.7 | 5.7 | 1.2 | 3.2 | 4.9 | 2.6 | 4.6 | 3.3 | 24.1 | 5.4 | 10.1 | 5.7
BactrianX-Llama | 10.9 | 11.6 | 8.9 | 12.3 | 8.8 | 22.0 | 32.1 | 8.5 | 12.1 | 25.1 | 11.4 | 6.9 | 6.4 | 8.2 | 4.1 | 10.9 | 8.7 | 14.1 | 4.3 | 8.4 | 15.2 | 8.0 | 11.4 | 10.8 | 19.4 | 16.6 | 23.4 | 12.6
AYA-23 | 9.3 | 10.5 | 8.0 | 11.6 | 6.9 | 14.2 | 17.5 | 5.6 | 8.3 | 18.3 | 11.3 | 5.7 | 4.0 | 5.9 | 2.7 | 8.1 | 7.6 | 12.2 | 3.3 | 9.0 | 8.8 | 6.5 | 10.4 | 6.8 | 24.3 | 10.6 | 17.7 | 9.8
AYA-101 | 26.4 | 26.8 | 14.6 | 21.6 | 12.6 | 49.3 | 46.6 | 33.3 | 25.8 | 49.5 | 38.8 | 12.2 | 25.9 | 37.2 | 4.4 | 17.8 | 13.4 | 29.7 | 17.6 | 13.2 | 23.3 | 20.4 | 35.6 | 22.2 | 36.5 | 36.9 | 41.9 | 27.2
SEA-LION | 7.2 | 8.1 | 6.5 | 9.3 | 5.8 | 12.5 | 17.1 | 4.9 | 7.0 | 13.9 | 7.9 | 5.3 | 7.0 | 9.6 | 2.0 | 7.6 | 6.0 | 9.5 | 4.8 | 6.6 | 8.4 | 4.9 | 8.0 | 5.9 | 21.2 | 10.3 | 14.1 | 8.6
SeaLLM v2.5 | 15.2 | 20.2 | 11.7 | 19.5 | 11.5 | 37.1 | 49.1 | 14.5 | 26.8 | 43.0 | 26.6 | 7.5 | 17.8 | 22.2 | 4.7 | 15.1 | 12.2 | 26.8 | 9.2 | 14.6 | 19.2 | 9.4 | 22.0 | 21.6 | 36.7 | 28.8 | 45.7 | 21.8
Sailor | 19.2 | 24.5 | 15.3 | 23.1 | 14.6 | 29.0 | 39.7 | 8.6 | 13.5 | 46.8 | 30.6 | 7.1 | 12.5 | 24.4 | 6.2 | 10.5 | 16.0 | 28.8 | 5.8 | 19.1 | 16.5 | 9.0 | 26.7 | 22.0 | 41.1 | 21.5 | 49.9 | 21.6
Cendol-mT5 | 8.3 | 11.4 | 14.2 | 11.6 | 6.9 | 7.2 | 8.4 | 4.7 | 5.5 | 35.8 | 17.5 | 4.0 | 6.3 | 8.5 | 2.0 | 5.2 | 6.1 | 10.5 | 2.9 | 8.8 | 6.6 | 4.1 | 17.1 | 5.5 | 4.4 | 6.4 | 20.5 | 9.3
Cendol-Llama2 | 8.6 | 10.0 | 14.4 | 19.3 | 6.6 | 6.9 | 8.2 | 6.4 | 6.4 | 36.1 | 19.1 | 5.5 | 3.0 | 4.3 | 4.1 | 4.5 | 14.1 | 22.0 | 1.9 | 17.5 | 5.4 | 4.8 | 17.3 | 3.4 | 8.1 | 7.6 | 22.0 | 10.6
Merak | 7.4 | 10.3 | 6.7 | 11.3 | 7.1 | 8.2 | 12.8 | 6.3 | 6.7 | 29.5 | 9.6 | 3.7 | 3.8 | 5.9 | 3.2 | 8.0 | 6.5 | 12.5 | 2.4 | 8.0 | 8.2 | 5.6 | 10.6 | 5.9 | 7.2 | 7.4 | 20.4 | 8.7
WangchanX-Llama3 | 19.8 | 24.4 | 14.3 | 28.9 | 13.4 | 42.2 | 48.6 | 12.7 | 29.4 | 50.1 | 29.4 | 7.7 | 18.1 | 19.7 | 6.0 | 17.6 | 15.6 | 30.0 | 10.4 | 18.1 | 22.4 | 13.9 | 28.0 | 25.1 | 39.2 | 35.5 | 45.4 | 24.7
Malaysian Llama3 | 15.2 | 17.3 | 12.3 | 22.2 | 11.1 | 19.7 | 24.0 | 8.7 | 12.6 | 38.6 | 19.4 | 7.2 | 6.7 | 9.0 | 5.9 | 10.6 | 12.4 | 23.5 | 4.2 | 14.3 | 13.9 | 8.3 | 19.0 | 14.2 | 17.3 | 15.6 | 44.4 | 15.8
Overall | 11.8 | 14.1 | 10.2 | 15.1 | 8.9 | 21.5 | 26.2 | 9.5 | 13.9 | 32.0 | 17.3 | 6.2 | 8.2 | 11.2 | 3.8 | 10.3 | 9.5 | 17.6 | 5.4 | 11.0 | 12.8 | 7.4 | 16.0 | 11.7 | 23.1 | 17.4 | 27.4 | 14.1

Table 26: NLG evaluation results in ROUGE-L per language.

Lang. | Subset | Original Task | Domain | # Samples
---|---|---|---|---
Translationese
eng | emotes_3k_eng_seacrowd_t2t | Commonsense Reasoning | Ethics | 2000
eng | aya_evaluation_suite_eng_seacrowd_t2t | Instruction Tuning | General | 400
ind | belebele_ind_latn_seacrowd_qa | QA | General | 1969
ind | parallel_asian_treebank_ind_eng_seacrowd_t2t | Machine Translation | News | 31
ind | aya_evaluation_suite_ind_seacrowd_t2t | Instruction Tuning | General | 4
ind | bactrian_x_id_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1972
ind | seaeval_cross_logiqa_ind_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 16
ind | seaeval_cross_mmlu_ind_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 8
khm | belebele_khm_khmr_seacrowd_qa | QA | General | 399
khm | khmer_alt_pos_seacrowd_seq_label | POS Tagging | News | 1595
khm | parallel_asian_treebank_khm_eng_seacrowd_t2t | Machine Translation | News | 6
khm | aya_evaluation_suite_khm_seacrowd_t2t | Instruction Tuning | General | 8
khm | bactrian_x_km_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1992
lao | belebele_lao_laoo_seacrowd_qa | QA | General | 1969
lao | parallel_asian_treebank_lao_eng_seacrowd_t2t | Machine Translation | News | 31
lao | aya_evaluation_suite_lao_seacrowd_t2t | Instruction Tuning | General | 400
mya | belebele_mya_mymr_seacrowd_qa | QA | General | 1969
mya | parallel_asian_treebank_mya_eng_seacrowd_t2t | Machine Translation | News | 31
mya | aya_evaluation_suite_mya_seacrowd_t2t | Instruction Tuning | General | 8
mya | bactrian_x_my_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1992
fil | belebele_tgl_latn_seacrowd_qa | QA | General | 2000
fil | bactrian_x_tl_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 2000
tha | belebele_tha_thai_seacrowd_qa | QA | General | 1969
tha | parallel_asian_treebank_tha_eng_seacrowd_t2t | Machine Translation | News | 31
tha | aya_evaluation_suite_tha_seacrowd_t2t | Instruction Tuning | General | 8
tha | bactrian_x_th_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1992
vie | belebele_vie_latn_seacrowd_qa | QA | General | 1969
vie | parallel_asian_treebank_vie_eng_seacrowd_t2t | Machine Translation | News | 31
vie | aya_evaluation_suite_vie_seacrowd_t2t | Instruction Tuning | General | 4
vie | bactrian_x_vi_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1972
vie | seaeval_cross_logiqa_vie_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 16
vie | seaeval_cross_mmlu_vie_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 8
zlm | belebele_zsm_latn_seacrowd_qa | QA | General | 1969
zlm | parallel_asian_treebank_zlm_eng_seacrowd_t2t | Machine Translation | News | 31
zlm | aya_evaluation_suite_zsm_seacrowd_t2t | Instruction Tuning | General | 400
zlm | seaeval_cross_logiqa_zlm_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 1056
zlm | seaeval_cross_mmlu_zlm_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 300
Natural
eng | cosem_seacrowd_ssp | Language Modeling | Social media | 2000
ind | sea_bench_ind_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 200
khm | gklmip_newsclass_seacrowd_text | Sentiment Analysis | E-commerce | 1436
khm | sea_bench_khm_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160
lao | sea_bench_lao_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160
mya | gklmip_sentiment_seacrowd_text | Sentiment Analysis | E-commerce | 716
mya | sea_bench_mya_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160
fil | sea_bench_tgl_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160
tha | sea_bench_tha_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 40
tha | vistec_tp_th_21_seacrowd_seq_label | NER | Social media | 1960
vie | sea_bench_vie_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 200
zlm | sea_bench_zlm_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160

Table 27: Train data used in the translationese classifier experiment.

Lang. | Subset | Original Task | Domain | # Samples
---|---|---|---|---
Translationese
eng | emotes_3k_eng_seacrowd_t2t | Commonsense Reasoning | Ethics | 2000
eng | aya_evaluation_suite_eng_seacrowd_t2t | Instruction Tuning | General | 400
ind | belebele_ind_latn_seacrowd_qa | QA | General | 1969
ind | parallel_asian_treebank_ind_eng_seacrowd_t2t | MT | News | 31
ind | aya_evaluation_suite_ind_seacrowd_t2t | Instruction Tuning | General | 4
ind | bactrian_x_id_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1972
ind | seaeval_cross_logiqa_ind_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 16
ind | seaeval_cross_mmlu_ind_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 8
khm | belebele_khm_khmr_seacrowd_qa | QA | General | 399
khm | khmer_alt_pos_seacrowd_seq_label | POS Tagging | News | 1595
khm | parallel_asian_treebank_khm_eng_seacrowd_t2t | MT | News | 6
khm | aya_evaluation_suite_khm_seacrowd_t2t | Instruction Tuning | General | 8
khm | bactrian_x_km_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1992
lao | belebele_lao_laoo_seacrowd_qa | QA | General | 1969
lao | parallel_asian_treebank_lao_eng_seacrowd_t2t | MT | News | 31
lao | aya_evaluation_suite_lao_seacrowd_t2t | Instruction Tuning | General | 400
mya | belebele_mya_mymr_seacrowd_qa | QA | General | 1969
mya | parallel_asian_treebank_mya_eng_seacrowd_t2t | MT | News | 31
mya | aya_evaluation_suite_mya_seacrowd_t2t | Instruction Tuning | General | 8
mya | bactrian_x_my_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1992
fil | belebele_tgl_latn_seacrowd_qa | QA | General | 2000
fil | bactrian_x_tl_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 2000
tha | belebele_tha_thai_seacrowd_qa | QA | General | 1969
tha | parallel_asian_treebank_tha_eng_seacrowd_t2t | MT | News | 31
tha | aya_evaluation_suite_tha_seacrowd_t2t | Instruction Tuning | General | 8
tha | bactrian_x_th_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1992
vie | belebele_vie_latn_seacrowd_qa | QA | General | 1969
vie | parallel_asian_treebank_vie_eng_seacrowd_t2t | MT | News | 31
vie | aya_evaluation_suite_vie_seacrowd_t2t | Instruction Tuning | General | 4
vie | bactrian_x_vi_seacrowd_t2t | Instruction Tuning | Mixed, Multi-domain, Wikipedia | 1972
vie | seaeval_cross_logiqa_vie_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 16
vie | seaeval_cross_mmlu_vie_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 8
zlm | belebele_zsm_latn_seacrowd_qa | QA | General | 1969
zlm | parallel_asian_treebank_zlm_eng_seacrowd_t2t | MT | News | 31
zlm | aya_evaluation_suite_zsm_seacrowd_t2t | Instruction Tuning | General | 400
zlm | seaeval_cross_logiqa_zlm_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 1056
zlm | seaeval_cross_mmlu_zlm_seacrowd_qa | Commonsense Reasoning, QA | Commentary, General, Multi-domain, Culture & heritage | 300
Natural
eng | cosem_seacrowd_ssp | Language Modeling | Social media | 2000
ind | sea_bench_ind_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 200
khm | gklmip_newsclass_seacrowd_text | Sentiment Analysis | E-commerce | 1436
khm | sea_bench_khm_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160
lao | sea_bench_lao_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160
mya | gklmip_sentiment_seacrowd_text | Sentiment Analysis | E-commerce | 716
mya | sea_bench_mya_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160
fil | sea_bench_tgl_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160
tha | sea_bench_tha_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 40
tha | vistec_tp_th_21_seacrowd_seq_label | NER | Social media | 1960
vie | sea_bench_vie_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 200
zlm | sea_bench_zlm_seacrowd_t2t | Instruction Tuning | Commentary, General, Multi-domain, Culture & heritage | 160

Table 28: Test data used in the translationese classifier experiment.

(a) $\tau=0.01$

(b) $\tau=0.2$

(c) $\tau=0.5$

(d) $\tau=0.7$

(e) $\tau=1.0$

Figure 10: Top-20 SEA indigenous languages to be prioritized based on their
potential demand and current utility.

(a) $\tau=0.01$

(b) $\tau=0.2$

(c) $\tau=0.5$

(d) $\tau=0.7$

(e) $\tau=1.0$

Figure 11: Top-20 SEA indigenous languages to be prioritized based on their
potential demand and data availability.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
1 | ind | Indonesian | Indonesia | <1B
2 | jav | Javanese | Indonesia | <100M
3 | vie | Vietnamese | Vietnam | <100M
4 | tha | Thai | Thailand, Cambodia | <100M
5 | fil | Filipino | Philippines | <100M
6 | mya | Burmese | Myanmar | <100M
7 | sun | Sunda | Indonesia | <100M
8 | tgl | Tagalog | Philippines | <100M
9 | khm | Khmer | Cambodia, Vietnam | <100M
10 | ceb | Cebuano | Philippines | <100M
11 | tts | Northeastern Thai | Thailand | <100M
12 | zlm | Malay | Malaysia | <100M
13 | zsm | Standard Malay | Malaysia, Brunei, Singapore | <100M

Table 29: SEA indigenous languages with $\geq$10M speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
1 | ilo | Ilocano | Philippines | <10M
2 | mad | Madura | Indonesia | <10M
3 | nod | Northern Thai | Laos, Thailand | <10M
4 | hil | Hiligaynon | Philippines | <10M
5 | min | Minangkabau | Indonesia | <10M
6 | bug | Bugis | Indonesia | <10M
7 | bew | Betawi | Indonesia | <10M
8 | sou | Southern Thai | Thailand | <10M
9 | lao | Lao | Cambodia, Laos | <10M
10 | hmv | Hmong Dô | Vietnam | <10M
11 | ace | Aceh | Indonesia | <10M
12 | bjn | Banjar | Indonesia | <10M
13 | ban | Bali | Indonesia | <10M
14 | shn | Shan | Myanmar, Thailand | <10M
15 | mui | Musi | Indonesia | <10M
16 | msi | Sabah Malay | Malaysia | <10M
17 | meo | Kedah Malay | Malaysia, Thailand | <10M
18 | pcc | Giáy | Vietnam | <10M
19 | war | Waray-Waray | Philippines | <10M
20 | mak | Makasar | Indonesia | <10M
21 | bcl | Central Bikol | Philippines | <10M
22 | xmm | Manado Malay | Indonesia | <10M
23 | sas | Sasak | Indonesia | <10M
24 | bbc | Batak Toba | Indonesia | <10M
25 | pam | Kapampangan | Philippines | <10M
26 | rki | Rakhine | Myanmar | <10M
27 | tyz | Tày | Vietnam | <10M
28 | abs | Ambonese Malay | Indonesia | <10M
29 | pse | Central Malay | Indonesia | <10M
30 | iba | Iban | Brunei, Indonesia, Malaysia | <10M
31 | kxm | Northern Khmer | Thailand | <10M
32 | khg | Khams Tibetan | Myanmar | <10M
33 | ksw | S’gaw Karen | Myanmar, Thailand | <10M
34 | btd | Batak Dairi | Indonesia | <10M
35 | bts | Batak Simalungun | Indonesia | <10M
36 | cbk | Chavacano | Philippines | <10M
37 | pag | Pangasinan | Philippines | <10M
38 | mtq | Muong | Vietnam | <10M
39 | btm | Batak Mandailing | Indonesia | <10M
40 | mdh | Maguindanaon | Philippines | <10M
41 | pmy | Papuan Malay | Indonesia | <10M
42 | gor | Gorontalo | Indonesia | <10M
43 | jax | Jambi Malay | Indonesia | <10M
44 | kjp | Pwo Eastern Karen | Myanmar, Thailand | <10M
45 | max | North Moluccan Malay | Indonesia | <10M
46 | mfa | Pattani Malay | Thailand | <10M
Not in SEACrowd
47 | mfp | Makassar Indonesian | Indonesia | <10M

Table 30: SEA indigenous languages with <10M speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
1 | nut | Nung | Vietnam | <1M
2 | kac | Jingpho | Myanmar | <1M
3 | tsg | Tausug | Philippines | <1M
4 | nij | Ngaju | Indonesia | <1M
5 | ljp | Lampung Api | Indonesia | <1M
6 | mqy | Manggarai | Indonesia | <1M
7 | mrw | Maranao | Philippines | <1M
8 | nia | Nias | Indonesia | <1M
9 | akb | Batak Angkola | Indonesia | <1M
10 | sda | Toraja-Sa’dan | Indonesia | <1M
11 | mnw | Mon | Myanmar, Thailand | <1M
12 | hni | Hani | Laos, Vietnam | <1M
13 | kjg | Khmu | Laos, Thailand, Vietnam | <1M
14 | aoz | Uab Meto | Indonesia | <1M
15 | blt | Tai Dam | Laos, Vietnam | <1M
16 | lus | Mizo Chin | Myanmar | <1M
17 | cps | Capiznon | Philippines | <1M
18 | btx | Batak Karo | Indonesia | <1M
19 | lis | Lisu | Myanmar | <1M
20 | msb | Masbatenyo | Philippines | <1M
21 | blk | Pa’o | Myanmar, Thailand | <1M
22 | tdd | Tai Nüa | Myanmar | <1M
23 | day | Land Dayak | Indonesia | <1M
24 | xdy | Malayic Dayak | Indonesia | <1M
25 | bhp | Bima | Indonesia | <1M
26 | ibg | Ibanag | Philippines | <1M
27 | zmi | Negeri Sembilan Malay | Malaysia | <1M
28 | mdr | Mandar | Indonesia | <1M
29 | kge | Komering | Indonesia | <1M
30 | bdr | West Coast Bajau | Malaysia | <1M
31 | kdt | Kuay | Cambodia, Laos, Thailand | <1M
32 | prk | Parauk Wa | Myanmar | <1M
33 | sgd | Surigaonon | Philippines | <1M
34 | tet | Tetun | East Timor, Indonesia | <1M
35 | bto | Rinconada Bikol | Philippines | <1M
36 | tdt | Tetun Dili | East Timor | <1M
37 | ium | Iu Mien | Laos, Vietnam | <1M
38 | krj | Kinaray-a | Philippines | <1M
39 | kyk | Kamayo | Philippines | <1M
40 | lew | Ledo Kaili | Indonesia | <1M
41 | mkn | Kupang Malay | Indonesia | <1M
42 | rej | Rejang | Indonesia | <1M
43 | mfb | Bangka | Indonesia | <1M
44 | rob | Tae’ | Indonesia | <1M
45 | lbw | Tolaki | Indonesia | <1M
46 | knx | Kendayan | Indonesia, Malaysia | <1M
47 | gay | Gayo | Indonesia | <1M
48 | mnb | Muna | Indonesia | <1M
49 | rbl | Miraya Bikol | Philippines | <1M
50 | smw | Sumbawa | Indonesia | <1M
51 | kxd | Brunei | Brunei | <1M
52 | khb | Lü | Laos, Myanmar | <1M
53 | lhu | Lahu | Laos, Myanmar | <1M
54 | twh | Tai Dón | Laos, Vietnam | <1M
55 | ysm | Myanmar Sign Language | Myanmar | <1M
56 | dtp | Kadazan Dusun | Malaysia | <1M
57 | fbl | West Albay Bikol | Philippines | <1M
58 | kvr | Kerinci | Indonesia | <1M
59 | pce | Ruching Palaung | Myanmar | <1M
60 | mry | Mandaya | Philippines | <1M
61 | nbe | Konyak Naga | Myanmar | <1M
62 | tcz | Thado Chin | Myanmar | <1M
63 | jra | Jarai | Cambodia, Vietnam | <1M
64 | xbr | Kambera | Indonesia | <1M
65 | mog | Mongondow | Indonesia | <1M
66 | pwo | Pwo Western Karen | Myanmar | <1M
67 | cja | Western Cham | Cambodia, Vietnam | <1M
68 | ahk | Akha | Laos, Myanmar, Thailand | <1M
69 | ssb | Southern Sama | Philippines | <1M
70 | sxn | Sangir | Indonesia | <1M

Table 31: (1/2) SEA indigenous languages with <1M speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
71 | btz | Batak Alas-Kluet | Indonesia | <1M
72 | ctd | Tedim Chin | Myanmar | <1M
73 | srv | Southern Sorsoganon | Philippines | <1M
74 | abl | Lampung Nyo | Indonesia | <1M
75 | dnw | Western Dani | Indonesia | <1M
76 | ktp | Kaduo | Laos | <1M
77 | slp | Lamaholot | Indonesia | <1M
78 | rad | Rade | Vietnam | <1M
79 | ski | Sika | Indonesia | <1M
80 | kpm | Koho | Vietnam | <1M
81 | bdq | Bahnar | Vietnam | <1M
82 | bdl | Indonesian Bajau | Indonesia | <1M
83 | bpr | Koronadal Blaan | Philippines | <1M
84 | ccp | Chakma | Myanmar | <1M
85 | kne | Kankanaey | Philippines | <1M
86 | kyu | Western Kayah | Myanmar | <1M
87 | mhy | Ma’anyan | Indonesia | <1M
88 | tnt | Tontemboan | Indonesia | <1M
89 | pll | Shwe Palaung | Myanmar | <1M
90 | daw | Davawenyo | Philippines | <1M
91 | cnh | Hakha Chin | Myanmar | <1M
92 | syb | Central Subanen | Philippines | <1M
93 | rbb | Rumai Palaung | Myanmar | <1M
94 | pmf | Pamona | Indonesia | <1M
95 | bln | Southern Catanduanes Bikol | Philippines | <1M
96 | itv | Itawit | Philippines | <1M
97 | pdu | Kayan | Myanmar | <1M
98 | mgm | Mambae | East Timor | <1M
99 | bhq | Tukang Besi South | Indonesia | <1M
100 | sly | Selayar | Indonesia | <1M
101 | mvp | Duri | Indonesia | <1M
102 | bgz | Banggai | Indonesia | <1M
103 | kjc | Coastal Konjo | Indonesia | <1M
104 | suc | Western Subanon | Philippines | <1M
105 | cyo | Cuyonon | Philippines | <1M
106 | khc | Tukang Besi North | Indonesia | <1M
107 | lhi | Lahu Shi | Myanmar | <1M
108 | mel | Central Melanau | Malaysia | <1M
109 | ibl | Ibaloi | Philippines | <1M
110 | end | Ende | Indonesia | <1M
111 | hvn | Hawu | Indonesia | <1M
112 | kkv | Kangean | Indonesia | <1M
113 | yka | Yakan | Philippines | <1M
114 | ljl | Li’o | Indonesia | <1M
115 | mkz | Makasae | East Timor | <1M
116 | bkd | Binukid | Philippines | <1M
117 | bkr | Bakumpai | Indonesia | <1M
118 | ekg | Ekari | Indonesia | <1M
119 | hnj | Hmong Njua | Laos, Thailand, Vietnam | <1M
120 | kak | Kalanguya | Philippines | <1M
121 | kkh | Khün | Myanmar | <1M
122 | lbx | Lawangan | Indonesia | <1M
123 | mhx | Lhao Vo | Myanmar | <1M
124 | mqj | Mamasa | Indonesia | <1M
125 | psp | Filipino Sign Language | Philippines | <1M
126 | tgn | Tandaganon | Philippines | <1M
Not in SEACrowd
127 | rhg | Rohingya | Myanmar | <1M
128 | pht | Phu Thai | Laos, Thailand, Vietnam | <1M
129 | tvn | Tavoyan | Myanmar | <1M
130 | osi | Osing | Indonesia | <1M
131 | ilp | Iranun | Philippines | <1M
132 | kzs | Sugut Dusun | Malaysia | <1M
133 | vkt | Tenggarong Kutai Malay | Indonesia | <1M
134 | phu | Phuan | Laos, Thailand | <1M
135 | csh | Asho Chin | Myanmar | <1M
136 | mlc | Cao Lan | Vietnam | <1M
137 | kjk | Highland Konjo | Indonesia | <1M
138 | liw | Col | Indonesia | <1M
139 | sss | So | Laos, Thailand | <1M
140 | dnv | Danu | Myanmar | <1M
141 | sdq | Semandang | Indonesia | <1M
142 | tjl | Tai Laing | Myanmar | <1M

Table 32: (2/2) SEA indigenous languages with <1M speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
1 | adr | Adonara | Indonesia | <100K
2 | sed | Sedang | Vietnam | <100K
3 | blf | Buol | Indonesia | <100K
4 | tbl | Tboli | Philippines | <100K
5 | hre | Hre | Vietnam | <100K
6 | rol | Romblomanon | Philippines | <100K
7 | akl | Aklanon | Philippines | <100K
8 | tdn | Tondano | Indonesia | <100K
9 | bps | Sarangani Blaan | Philippines | <100K
10 | kqr | Kimaragang | Malaysia | <100K
11 | sml | Central Sama | Philippines | <100K
12 | txs | Tonsea | Indonesia | <100K
13 | stb | Northern Subanen | Philippines | <100K
14 | bks | Northern Sorsoganon | Philippines | <100K
15 | kei | Kei | Indonesia | <100K
16 | klg | Tagakaulo | Philippines | <100K
17 | tld | Talaud | Indonesia | <100K
18 | atb | Zaiwa | Myanmar | <100K
19 | sse | Balangingih Sama | Philippines | <100K
20 | tes | Tengger | Indonesia | <100K
21 | tyr | Tai Daeng | Laos, Vietnam | <100K
22 | cia | Cia-Cia | Indonesia | <100K
23 | gbi | Galela | Indonesia | <100K
24 | otd | Ot Danum | Indonesia | <100K
25 | cts | Northern Catanduanes Bikol | Philippines | <100K
26 | loe | Saluan | Indonesia | <100K
27 | bno | Bantoanon | Philippines | <100K
28 | cmr | Mro-Khimi | Myanmar | <100K
29 | ubl | Buhi’non Bikol | Philippines | <100K
30 | cjm | Eastern Cham | Vietnam | <100K
31 | bkx | Baikeno | East Timor | <100K
32 | aaz | Amarasi | Indonesia | <100K
33 | bhw | Biak | Indonesia | <100K
34 | kqe | Kalagan | Philippines | <100K
35 | xnn | Northern Kankanay | Philippines | <100K
36 | xsb | Sambal | Philippines | <100K
37 | cfm | Falam Chin | Myanmar | <100K
38 | lbl | Libon Bikol | Philippines | <100K
39 | wlo | Wolio | Indonesia | <100K
40 | bth | Biatah Bidayuh | Indonesia, Malaysia | <100K
41 | kem | Kemak | East Timor, Indonesia | <100K
42 | raw | Rawang | Myanmar | <100K
43 | tft | Ternate | Indonesia | <100K
44 | zom | Zo | Myanmar | <100K
45 | cnk | Khumi Chin | Myanmar | <100K
46 | mqx | Mamuju | Indonesia | <100K
47 | msm | Agusan Manobo | Philippines | <100K
48 | nst | Tangshang Naga | Myanmar | <100K
49 | nxg | Ngad’a | Indonesia | <100K
50 | obo | Obo Manobo | Philippines | <100K
51 | pww | Pwo Northern Karen | Thailand | <100K
52 | sya | Siang | Indonesia | <100K
53 | tom | Tombulu | Indonesia | <100K
54 | xml | Malaysian Sign Language | Malaysia | <100K
55 | mbs | Sarangani Manobo | Philippines | <100K
56 | mwv | Mentawai | Indonesia | <100K
57 | msk | Mansaka | Philippines | <100K
58 | smk | Bolinao | Philippines | <100K
59 | bfn | Bunak | East Timor, Indonesia | <100K
60 | bgi | Bagobo-Klata | Philippines | <100K
61 | drg | Rungus | Malaysia | <100K
62 | kzf | Da’a Kaili | Indonesia | <100K
63 | wew | Wejewa | Indonesia | <100K
64 | rog | Northern Roglai | Vietnam | <100K
65 | ilk | Bogkalot | Philippines | <100K
66 | ktv | Eastern Katu | Vietnam | <100K
67 | dnt | Mid Grand Valley Dani | Indonesia | <100K
68 | frd | Fordata | Indonesia | <100K
69 | mbt | Matigsalug Manobo | Philippines | <100K
70 | nxe | Nage | Indonesia | <100K
71 | ptt | Enrekang | Indonesia | <100K

Table 33: (1/5) SEA indigenous languages with <100K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
72 | tiy | Teduray | Philippines | <100K
73 | tjg | Tunjung | Indonesia | <100K
74 | wmm | Maiwa | Indonesia | <100K
75 | sdo | Bukar-Sadong Bidayuh | Indonesia, Malaysia | <100K
76 | kyp | Kang | Laos | <100K
77 | tvo | Tidore | Indonesia | <100K
78 | hos | Ho Chi Minh City Sign Language | Vietnam | <100K
79 | mhs | Buru | Indonesia | <100K
80 | sti | Bulo Stieng | Cambodia, Vietnam | <100K
81 | law | Lauje | Indonesia | <100K
82 | bgs | Tagabawa | Philippines | <100K
83 | sjm | Mapun | Philippines | <100K
84 | blr | Blang | Myanmar, Thailand | <100K
85 | rgs | Southern Roglai | Vietnam | <100K
86 | smr | Simeulue | Indonesia | <100K
87 | czt | Zotung Chin | Myanmar | <100K
88 | kvq | Geba Karen | Myanmar | <100K
89 | mtd | Mualang | Indonesia | <100K
90 | xxk | Ke’o | Indonesia | <100K
91 | tkd | Tukudede | East Timor | <100K
92 | kix | Khiamniungan Naga | Myanmar | <100K
93 | bsb | Brunei Bisaya | Brunei, Malaysia | <100K
94 | dao | Daai Chin | Myanmar | <100K
95 | ddg | Fataluku | East Timor | <100K
96 | mqn | Moronene | Indonesia | <100K
97 | ges | Geser-Gorom | Indonesia | <100K
98 | pho | Phunoi | Laos | <100K
99 | slm | Pangutaran Sama | Philippines | <100K
100 | hro | Haroi | Vietnam | <100K
101 | ivv | Ivatan | Philippines | <100K
102 | mrh | Mara Chin | Myanmar | <100K
103 | btw | Butuanon | Philippines | <100K
104 | cma | Maa | Vietnam | <100K
105 | sbl | Botolan Sambal | Philippines | <100K
106 | cmo | Central Mnong | Cambodia, Vietnam | <100K
107 | blz | Balantak | Indonesia | <100K
108 | tpu | Tampuan | Cambodia | <100K
109 | blj | Bulungan | Indonesia | <100K
110 | cgc | Kagayanen | Philippines | <100K
111 | clu | Caluyanun | Philippines | <100K
112 | cml | Koneq-koneq | Indonesia | <100K
113 | gad | Gaddang | Philippines | <100K
114 | hlt | Matu Chin | Myanmar | <100K
115 | ifk | Tuwali Ifugao | Philippines | <100K
116 | ifu | Mayoyao Ifugao | Philippines | <100K
117 | knb | Lubuagan Kalinga | Philippines | <100K
118 | ksx | Kedang | Indonesia | <100K
119 | lcf | Lubu | Indonesia | <100K
120 | lsi | Lacid | Myanmar | <100K
121 | mba | Higaonon | Philippines | <100K
122 | mng | Eastern Mnong | Vietnam | <100K
123 | mro | Mru | Myanmar | <100K
124 | mta | Cotabato Manobo | Philippines | <100K
125 | set | Sentani | Indonesia | <100K
126 | tmn | Taman | Indonesia | <100K
127 | twu | Termanu | Indonesia | <100K
128 | txm | Tomini | Indonesia | <100K
129 | ulm | Ulumanda’ | Indonesia | <100K
130 | wow | Wawonii | Indonesia | <100K
131 | sne | Bau Bidayuh | Indonesia, Malaysia | <100K
132 | tdf | Talieng | Laos | <100K
133 | lbo | Laven | Laos | <100K
134 | acn | Ngochang | Myanmar | <100K
135 | tlb | Tobelo | Indonesia | <100K
136 | ifa | Amganad Ifugao | Philippines | <100K
137 | itd | Southern Tidung | Indonesia, Malaysia | <100K
138 | pha | Pa-Hng | Vietnam | <100K
139 | atd | Ata Manobo | Philippines | <100K
140 | bru | Eastern Bru | Laos, Vietnam | <100K
141 | kzp | Kaidipang | Indonesia | <100K
142 | abx | Inabaknon | Philippines | <100K

Table 34: (2/5) SEA indigenous languages with <100K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
143 | aol | Alor | Indonesia | <100K
144 | jmd | Yamdena | Indonesia | <100K
145 | laa | Southern Subanen | Philippines | <100K
146 | lmy | Lamboya | Indonesia | <100K
147 | txe | Totoli | Indonesia | <100K
148 | oyb | Oy | Laos | <100K
149 | mlf | Mal | Laos, Thailand | <100K
150 | lnd | Lundayeh | Brunei, Indonesia, Malaysia | <100K
151 | prh | Porohanon | Philippines | <100K
152 | brb | Brao | Cambodia, Laos, Vietnam | <100K
153 | lbn | Rmeet | Laos | <100K
154 | ilm | Iranun | Malaysia | <100K
155 | ptu | Bambam | Indonesia | <100K
156 | vkl | Kulisusu | Indonesia | <100K
157 | blw | Balangao | Philippines | <100K
158 | bsy | Sabah Bisaya | Malaysia | <100K
159 | krr | Krung | Cambodia | <100K
160 | dtb | Labuk-Kinabatangan Kadazan | Malaysia | <100K
161 | ayz | Mai Brat | Indonesia | <100K
162 | bac | Badui | Indonesia | <100K
163 | brv | Western Bru | Laos, Thailand | <100K
164 | bwp | Mandobo Bawah | Indonesia | <100K
165 | dna | Upper Grand Valley Dani | Indonesia | <100K
166 | dni | Lower Grand Valley Dani | Indonesia | <100K
167 | dtr | Lotud | Malaysia | <100K
168 | dun | Dusun Deyah | Indonesia | <100K
169 | kje | Kisar | Indonesia | <100K
170 | kli | Kalumpang | Indonesia | <100K
171 | kod | Kodi | Indonesia | <100K
172 | llg | Lole | Indonesia | <100K
173 | lrt | Larantuka Malay | Indonesia | <100K
174 | mnz | Moni | Indonesia | <100K
175 | pea | Peranakan Indonesian | Indonesia | <100K
176 | ppk | Uma | Indonesia | <100K
177 | prt | Prai | Laos, Thailand | <100K
178 | tmm | Tai Thanh | Vietnam | <100K
179 | tnw | Tonsawang | Indonesia | <100K
180 | twy | Tawoyan | Indonesia | <100K
181 | txq | Tii | Indonesia | <100K
182 | wlw | Walak | Indonesia | <100K
183 | skh | Sikule | Indonesia | <100K
184 | lbk | Central Bontok | Philippines | <100K
185 | cje | Chru | Vietnam | <100K
186 | hnn | Hanunoo | Philippines | <100K
187 | tlu | Tulehu | Indonesia | <100K
188 | wmh | Waima’a | East Timor | <100K
189 | hrk | Haruku | Indonesia | <100K
190 | lex | Luang | Indonesia | <100K
191 | puo | Puoc | Vietnam | <100K
192 | ren | Rengao | Vietnam | <100K
193 | alp | Alune | Indonesia | <100K
194 | bwe | Bwe Karen | Myanmar | <100K
195 | tlt | Sou Nama | Indonesia | <100K
196 | zyp | Zyphe Chin | Myanmar | <100K
197 | abz | Abui | Indonesia | <100K
198 | akg | Anakalangu | Indonesia | <100K
199 | had | Hatam | Indonesia | <100K
200 | htu | Hitu | Indonesia | <100K
201 | nlc | Nalca | Indonesia | <100K
202 | pac | Pacoh | Laos, Vietnam | <100K
203 | yog | Yogad | Philippines | <100K
204 | mxd | Modang | Indonesia | <100K
205 | jeh | Jeh | Laos, Vietnam | <100K
206 | kyn | Northern Binukidnon | Philippines | <100K
207 | phg | Phuong | Vietnam | <100K
208 | agn | Agutaynen | Philippines | <100K
209 | cnw | Ngawn Chin | Myanmar | <100K
210 | ila | Ile Ape | Indonesia | <100K
211 | krd | Kairui-Midiki | East Timor | <100K
212 | loa | Loloda | Indonesia | <100K
213 | mbb | Western Bukidnon Manobo | Philippines | <100K
214 | mwq | Müün Chin | Myanmar | <100K
215 | nxa | Nauete | East Timor | <100K
216 | prf | Paranan | Philippines | <100K

Table 35: (3/5) SEA indigenous languages with <100K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
217 | snl | Sangil | Philippines | <100K
218 | tby | Tabaru | Indonesia | <100K
219 | tea | Temiar | Malaysia | <100K
220 | yli | Angguruk Yali | Indonesia | <100K
221 | mej | Meyah | Indonesia | <100K
222 | mbi | Ilianen Manobo | Philippines | <100K
223 | plw | Brooke’s Point Palawano | Philippines | <100K
224 | duu | Drung | Myanmar | <100K
225 | heg | Helong | Indonesia | <100K
226 | mzq | Mori Atas | Indonesia | <100K
227 | uhn | Damal | Indonesia | <100K
228 | xmz | Mori Bawah | Indonesia | <100K
229 | kjm | Kháng | Vietnam | <100K
230 | hal | Salang | Laos, Vietnam | <100K
231 | idt | Idaté | East Timor | <100K
232 | dok | Dondo | Indonesia | <100K
233 | gal | Galolen | East Timor, Indonesia | <100K
234 | ksc | Southern Kalinga | Philippines | <100K
235 | txa | Tombonuo | Malaysia | <100K
236 | ngt | Kriang | Laos | <100K
237 | kmk | Limos Kalinga | Philippines | <100K
238 | alo | Larike-Wakasihu | Indonesia | <100K
239 | yno | Yong | Thailand | <100K
240 | ril | Riang Lang | Myanmar | <100K
241 | atq | Aralle-Tabulahan | Indonesia | <100K
242 | cek | Eastern Khumi Chin | Myanmar | <100K
243 | cua | Cua | Vietnam | <100K
244 | mnx | Sougb | Indonesia | <100K
245 | mqs | West Makian | Indonesia | <100K
246 | nuf | Nusu | Myanmar | <100K
247 | plc | Central Palawano | Philippines | <100K
248 | plv | Southwest Palawano | Philippines | <100K
249 | rgu | Rikou | Indonesia | <100K
250 | szw | Sawai | Indonesia | <100K
251 | tdj | Tajio | Indonesia | <100K
252 | xkl | Mainstream Kenyah | Indonesia, Malaysia | <100K
253 | yin | Riang Lai | Myanmar | <100K
254 | lcl | Lisela | Indonesia | <100K
255 | lra | Rara Bakati’ | Indonesia, Malaysia | <100K
256 | bve | Berau Malay | Indonesia | <100K
257 | kml | Tanudan Kalinga | Philippines | <100K
258 | beu | Blagar | Indonesia | <100K
259 | xem | Mateq | Indonesia | <100K
260 | lev | Western Pantar | Indonesia | <100K
261 | ptn | Patani | Indonesia | <100K
262 | oog | Ong | Laos | <100K
263 | spr | Saparua | Indonesia | <100K
264 | amk | Ambai | Indonesia | <100K
265 | ifb | Batad Ifugao | Philippines | <100K
266 | aax | Mandobo Atas | Indonesia | <100K
267 | bep | Behoa | Indonesia | <100K
268 | bvy | Baybayanon | Philippines | <100K
269 | csy | Siyin Chin | Myanmar | <100K
270 | dbj | Ida’an | Malaysia | <100K
271 | emb | Embaloh | Indonesia | <100K
272 | iry | Iraya | Philippines | <100K
273 | jak | Jakun | Malaysia | <100K
274 | jaq | Yaqay | Indonesia | <100K
275 | kps | Tehit | Indonesia | <100K
276 | kvb | Kubu | Indonesia | <100K
277 | kxf | Kawyaw | Myanmar | <100K
278 | kyt | Kayagar | Indonesia | <100K
279 | lje | Rampi | Indonesia | <100K
280 | lur | Loura | Indonesia | <100K
281 | mbd | Dibabawon Manobo | Philippines | <100K
282 | mbf | Baba Malay | Singapore | <100K
283 | mky | East Makian | Indonesia | <100K
284 | mvd | Mamboru | Indonesia | <100K
285 | ndx | Nduga | Indonesia | <100K
286 | pez | Eastern Penan | Brunei, Malaysia | <100K
287 | ple | Palu’e | Indonesia | <100K
288 | sea | Semai | Malaysia | <100K
289 | ssq | So’a | Indonesia | <100K

Table 36: (4/5) SEA indigenous languages with <100K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
290 | szb | Ngalum | Indonesia | <100K
291 | tbk | Calamian Tagbanwa | Philippines | <100K
292 | tbw | Tagbanwa | Philippines | <100K
293 | txx | Tatana | Malaysia | <100K
294 | wnk | Wanukaka | Indonesia | <100K
295 | yva | Yawa | Indonesia | <100K
Not in SEACrowd
296 | int | Intha | Myanmar | <100K
297 | loc | Inonhan | Philippines | <100K
298 | mqg | Kota Bangun Kutai Malay | Indonesia | <100K
299 | bfx | Bantayanon | Philippines | <100K
300 | tou | Tho | Vietnam | <100K
301 | ncq | Northern Katang | Laos | <100K
302 | bvu | Bukit Malay | Indonesia | <100K
303 | byd | Benyadu’ | Indonesia | <100K
304 | tsq | Thai Sign Language | Thailand | <100K
305 | nyw | Nyaw | Thailand | <100K
306 | rir | Ribun | Indonesia | <100K
307 | scg | Sanggau | Indonesia | <100K
308 | sct | Southern Katang | Laos | <100K
309 | stt | Budeh Stieng | Vietnam | <100K
310 | tco | Taungyo | Myanmar | <100K
311 | vkk | Kaur | Indonesia | <100K
312 | hab | Hanoi Sign Language | Vietnam | <100K
313 | djo | Jangkang | Indonesia | <100K
314 | sbx | Seberuang | Indonesia | <100K
315 | lso | Laos Sign Language | Laos | <100K
316 | sez | Senthang Chin | Myanmar | <100K
317 | soa | Thai Song | Thailand | <100K
318 | knl | Keninjal | Indonesia | <100K
319 | tth | Upper Ta’oih | Laos, Vietnam | <100K
320 | apg | Ampanang | Indonesia | <100K
321 | mnn | Southern Mnong | Vietnam | <100K
322 | pel | Pekal | Indonesia | <100K
323 | zkd | Kadu | Myanmar | <100K
324 | bkz | Bungku | Indonesia | <100K
325 | mkx | Kinamiging Manobo | Philippines | <100K
326 | bnu | Bentong | Indonesia | <100K
327 | kxy | Kayong | Vietnam | <100K
328 | mhp | Balinese Malay | Indonesia | <100K
329 | unz | Unde Kaili | Indonesia | <100K
330 | bld | Bolango | Indonesia | <100K
331 | kuf | Western Katu | Laos | <100K
332 | dnk | Dengka | Indonesia | <100K
333 | mvv | Tagal Murut | Indonesia, Malaysia | <100K
334 | skn | Kolibugan Subanon | Philippines | <100K
335 | szn | Sula | Indonesia | <100K
336 | cnb | Uppu Chin | Myanmar | <100K
337 | bhv | Bahau | Indonesia | <100K
338 | itt | Maeng Itneg | Philippines | <100K
339 | hji | Haji | Indonesia | <100K
340 | ghk | Geko Karen | Myanmar | <100K
341 | kvl | Kayaw | Myanmar | <100K
342 | tto | Lower Ta’oih | Laos | <100K
343 | bdb | Basap | Indonesia | <100K
344 | clj | Laitu Chin | Myanmar | <100K
345 | clt | Lautu Chin | Myanmar | <100K
346 | dup | Duano | Indonesia, Malaysia | <100K
347 | kyb | Butbut Kalinga | Philippines | <100K
348 | stg | Trieng | Vietnam | <100K
349 | cbw | Kinabalian | Philippines | <100K
350 | csv | Sumtu Chin | Myanmar | <100K
351 | riu | Riung | Indonesia | <100K
352 | srg | Sulod | Philippines | <100K
353 | ity | Moyadan Itneg | Philippines | <100K
354 | kkg | Mabaka Valley Kalinga | Philippines | <100K
355 | bne | Bintauna | Indonesia | <100K
356 | nlk | Ninia Yali | Indonesia | <100K
357 | hik | Seit-Kaitetu | Indonesia | <100K
358 | ksn | Kasiguranin | Philippines | <100K
359 | tsl | Ts’ün-Lao | Vietnam | <100K
360 | xao | Khao | Vietnam | <100K

Table 37: (5/5) SEA indigenous languages with <100K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
1 | xte | Ketengban | Indonesia | <10K
2 | bna | Bonerate | Indonesia | <10K
3 | bku | Buhid | Philippines | <10K
4 | aws | South Awyu | Indonesia | <10K
5 | woo | Manombai | Indonesia | <10K
6 | asc | Casuarina Coast Asmat | Indonesia | <10K
7 | tih | Timugon Murut | Malaysia | <10K
8 | asl | Asilulu | Indonesia | <10K
9 | sgb | Mag-antsi Ayta | Philippines | <10K
10 | eky | Eastern Kayah | Myanmar, Thailand | <10K
11 | ify | Keley-i Kallahan | Philippines | <10K
12 | inl | Indonesian Sign Language | Indonesia | <10K
13 | kgq | Kamoro | Indonesia | <10K
14 | kht | Khamti | Myanmar | <10K
15 | kpq | Korupun-Sela | Indonesia | <10K
16 | kti | North Muyu | Indonesia | <10K
17 | lcp | Western Lawa | Thailand | <10K
18 | mtj | Moskona | Indonesia | <10K
19 | slu | Selaru | Indonesia | <10K
20 | tmw | Temuan | Malaysia | <10K
21 | txt | Citak | Indonesia | <10K
22 | whk | Wahau Kenyah | Indonesia | <10K
23 | txn | West Tarangan | Indonesia | <10K
24 | dro | Daro-Matu Melanau | Malaysia | <10K
25 | awu | Central Awyu | Indonesia | <10K
26 | itb | Binongan Itneg | Philippines | <10K
27 | lti | Leti | Indonesia | <10K
28 | saj | Sahu | Indonesia | <10K
29 | kvv | Kola | Indonesia | <10K
30 | kvu | Yinbaw | Myanmar | <10K
31 | akc | Mpur | Indonesia | <10K
32 | cns | Central Asmat | Indonesia | <10K
33 | crw | Chrau | Vietnam | <10K
34 | lwl | Eastern Lawa | Thailand | <10K
35 | lzn | Lainong Naga | Myanmar | <10K
36 | mrz | Marind | Indonesia | <10K
37 | row | Dela-Oenale | Indonesia | <10K
38 | sfe | Eastern Subanen | Philippines | <10K
39 | ttd | Tutong | Brunei | <10K
40 | iwo | Morop | Indonesia | <10K
41 | twb | Tawbuid | Philippines | <10K
42 | bhz | Bada | Indonesia | <10K
43 | pwm | Molbog | Malaysia, Philippines | <10K
44 | psa | Asue Awyu | Indonesia | <10K
45 | ebk | Eastern Bontok | Philippines | <10K
46 | tre | East Tarangan | Indonesia | <10K
47 | npy | Napu | Indonesia | <10K
48 | gdg | Ga’dang | Philippines | <10K
49 | gir | Red Gelao | Vietnam | <10K
50 | kll | Kagan Kalagan | Philippines | <10K
51 | lwt | Lewotobi | Indonesia | <10K
52 | moo | Monom | Vietnam | <10K
53 | pnp | Pancana | Indonesia | <10K
54 | tdr | Todrah | Vietnam | <10K
55 | weo | Wemale | Indonesia | <10K
56 | woi | Kamang | Indonesia | <10K
57 | wrp | Waropen | Indonesia | <10K
58 | lha | Laha | Vietnam | <10K
59 | kvo | Dobel | Indonesia | <10K
60 | mtg | Una | Indonesia | <10K
61 | inn | Isinay | Philippines | <10K
62 | ihp | Iha | Indonesia | <10K
63 | jka | Kaera | Indonesia | <10K
64 | myl | Moma | Indonesia | <10K
65 | mmn | Minamanwa | Philippines | <10K
66 | nxr | Ninggerum | Indonesia | <10K
67 | blx | Mag-Indi Ayta | Philippines | <10K
68 | duw | Dusun Witu | Indonesia | <10K
69 | kgw | Karon Dori | Indonesia | <10K
70 | kyo | Klon | Indonesia | <10K
71 | lbt | Lachi | Vietnam | <10K
72 | mli | Malimpung | Indonesia | <10K
73 | nfa | Dhao | Indonesia | <10K
74 | pdo | Padoe | Indonesia | <10K
75 | raz | Rahambuu | Indonesia | <10K
76 | tpg | Kula | Indonesia | <10K
77 | urk | Urak Lawoi’ | Thailand | <10K
78 | wad | Wamesa | Indonesia | <10K
79 | wod | Wolani | Indonesia | <10K
80 | wul | Silimo | Indonesia | <10K

Table 38: (1/6) SEA indigenous languages with <10K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
81 | yac | Pass Valley Yali | Indonesia | <10K
82 | yoy | Yoy | Laos, Thailand | <10K
83 | and | Ansus | Indonesia | <10K
84 | mxn | Moi Kelim | Indonesia | <10K
85 | tlv | Taliabu | Indonesia | <10K
86 | bty | Bobot | Indonesia | <10K
87 | duq | Dusun Malang | Indonesia | <10K
88 | ums | Pendau | Indonesia | <10K
89 | vbb | Southeast Babar | Indonesia | <10K
90 | baj | Barakai | Indonesia | <10K
91 | bgr | Bawm Chin | Myanmar | <10K
92 | irr | Ir | Laos | <10K
93 | nbq | Nggem | Indonesia | <10K
94 | bqr | Burusu | Indonesia | <10K
95 | kvd | Kui | Indonesia | <10K
96 | bny | Bintulu | Malaysia | <10K
97 | rka | Kraol | Cambodia | <10K
98 | jah | Jah Hut | Malaysia | <10K
99 | kys | Baram Kayan | Malaysia | <10K
100 | smu | Somray | Cambodia | <10K
101 | sza | Semelai | Malaysia | <10K
102 | alk | Alak | Laos | <10K
103 | anl | Anu-Khongso Chin | Myanmar | <10K
104 | bei | Bakati’ | Indonesia | <10K
105 | irh | Irarutu | Indonesia | <10K
106 | kta | Katua | Vietnam | <10K
107 | kts | South Muyu | Indonesia | <10K
108 | kzi | Kelabit | Indonesia, Malaysia | <10K
109 | lmr | Lamalera | Indonesia | <10K
110 | mwt | Moken | Myanmar, Thailand | <10K
111 | ntx | Tangkhul Naga | Myanmar | <10K
112 | ror | Rongga | Indonesia | <10K
113 | sdu | Sarudu | Indonesia | <10K
114 | slz | Ma’ya | Indonesia | <10K
115 | sre | Sara Bakati’ | Indonesia | <10K
116 | tgb | Tobilung | Malaysia | <10K
117 | twe | Teiwa | Indonesia | <10K
118 | tyn | Kombai | Indonesia | <10K
119 | wah | Watubela | Indonesia | <10K
120 | nev | Nyaheun | Laos | <10K
121 | klz | Kabola | Indonesia | <10K
122 | awy | Edera Awyu | Indonesia | <10K
123 | abd | Manide | Philippines | <10K
124 | tnm | Tabla | Indonesia | <10K
125 | skb | Saek | Laos, Thailand | <10K
126 | kvw | Wersing | Indonesia | <10K
127 | xod | Kokoda | Indonesia | <10K
128 | bpq | Banda Malay | Indonesia | <10K
129 | bay | Batuley | Indonesia | <10K
130 | kgx | Kamaru | Indonesia | <10K
131 | khe | Korowai | Indonesia | <10K
132 | lkj | Remun | Malaysia | <10K
133 | pku | Paku | Indonesia | <10K
134 | saw | Sawi | Indonesia | <10K
135 | tcg | Tamagario | Indonesia | <10K
136 | pne | Western Penan | Malaysia | <10K
137 | xks | Kumbewaha | Indonesia | <10K
138 | pgu | Pagu | Indonesia | <10K
139 | tpo | Tai Pao | Laos, Vietnam | <10K
140 | zrs | Mairasi | Indonesia | <10K
141 | kzz | Kalabra | Indonesia | <10K
142 | bls | Balaesang | Indonesia | <10K
143 | kuv | Kur | Indonesia | <10K
144 | ree | Rejang Kayan | Malaysia | <10K
145 | abp | Abellen Ayta | Philippines | <10K
146 | adn | Adang | Indonesia | <10K
147 | ahh | Aghu | Indonesia | <10K
148 | bnd | Banda | Indonesia | <10K
149 | bnq | Bantik | Indonesia | <10K
150 | ckh | Chak | Myanmar | <10K
151 | due | Umiray Dumaget Agta | Philippines | <10K
152 | eip | Lik | Indonesia | <10K
153 | kgr | Abun | Indonesia | <10K
154 | kig | Kimaghima | Indonesia | <10K
155 | nsy | Nasal | Indonesia | <10K
156 | swt | Sawila | Indonesia | <10K
157 | tmg | Ternateño | Indonesia | <10K
158 | wms | Wambon | Indonesia | <10K
159 | mhe | Mah Meri | Malaysia | <10K
160 | bgl | Bo | Laos | <10K

Table 39: (2/6) SEA indigenous languages with <10k speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
161 | bpv | Bian Marind | Indonesia | <10K
162 | gzn | Gane | Indonesia | <10K
163 | dmr | East Damar | Indonesia | <10K
164 | obk | Southern Bontok | Philippines | <10K
165 | bzl | Boano | Indonesia | <10K
166 | hbu | Habun | East Timor | <10K
167 | zng | Mang | Vietnam | <10K
168 | gei | Gebe | Indonesia | <10K
169 | spb | Sepa | Indonesia | <10K
170 | agv | Remontado Dumagat | Philippines | <10K
171 | bzq | Buli | Indonesia | <10K
172 | brp | Barapasi | Indonesia | <10K
173 | cbl | Bualkhaw Chin | Myanmar | <10K
174 | grs | Gresi | Indonesia | <10K
175 | jmn | Makuri Naga | Myanmar | <10K
176 | kmt | Kemtuik | Indonesia | <10K
177 | kwe | Kwerba | Indonesia | <10K
178 | sko | Seko Tengah | Indonesia | <10K
179 | wrs | Waris | Indonesia | <10K
180 | kyi | Kiput | Malaysia | <10K
181 | nrm | Narom | Malaysia | <10K
182 | klw | Tado | Indonesia | <10K
183 | spu | Sapuan | Laos | <10K
184 | jei | Yei | Indonesia | <10K
185 | sqq | Sou | Laos | <10K
186 | awv | Jair Awyu | Indonesia | <10K
187 | bup | Busoa | Indonesia | <10K
188 | kkl | Kosarek Yale | Indonesia | <10K
189 | zka | Kaimbulawa | Indonesia | <10K
190 | kjr | Kurudu | Indonesia | <10K
191 | alj | Alangan | Philippines | <10K
192 | asy | Yaosakor Asmat | Indonesia | <10K
193 | dms | Dampelas | Indonesia | <10K
194 | enr | Emem | Indonesia | <10K
195 | hnu | Hung | Laos, Vietnam | <10K
196 | kwt | Kwesten | Indonesia | <10K
197 | kyj | Karao | Philippines | <10K
198 | lau | Laba | Indonesia | <10K
199 | ley | Limola | Indonesia | <10K
200 | mqf | Momuna | Indonesia | <10K
201 | mqo | Modole | Indonesia | <10K
202 | nir | Nimboran | Indonesia | <10K
203 | pmo | Pom | Indonesia | <10K
204 | sge | Segai | Indonesia | <10K
205 | szc | Semaq Beri | Malaysia | <10K
206 | tgt | Central Tagbanwa | Philippines | <10K
207 | tty | Sikaritai | Indonesia | <10K
208 | bgk | Bit | Laos | <10K
209 | grm | Kota Marudu Talantang | Malaysia | <10K
210 | srl | Isirawa | Indonesia | <10K
211 | wbw | Woi | Indonesia | <10K
212 | sib | Sebop | Malaysia | <10K
213 | bnb | Bookan Murut | Malaysia | <10K
214 | llm | Lasalimu | Indonesia | <10K
215 | rmm | Roma | Indonesia | <10K
216 | pcb | Pear | Cambodia | <10K
217 | abc | Ambala Ayta | Philippines | <10K
218 | nxx | Nafri | Indonesia | <10K
219 | lwh | White Lachi | Vietnam | <10K
220 | ury | Orya | Indonesia | <10K
221 | irx | Kamberau | Indonesia | <10K
222 | atk | Ati | Philippines | <10K
223 | bgb | Bobongko | Indonesia | <10K
224 | bvz | Bauzi | Indonesia | <10K
225 | bzp | Kemberano | Indonesia | <10K
226 | cbn | Nyahkur | Thailand | <10K
227 | dbf | Edopi | Indonesia | <10K
228 | eno | Enggano | Indonesia | <10K
229 | mkm | Moklen | Thailand | <10K
230 | nxl | South Nuaulu | Indonesia | <10K
231 | vko | Kodeoha | Indonesia | <10K
232 | wbb | Wabo | Indonesia | <10K
233 | yir | North Awyu | Indonesia | <10K
234 | zbc | Central Berawan | Malaysia | <10K
235 | bya | Batak | Philippines | <10K

Table 40: (3/6) SEA indigenous languages with <10K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
236 | bdg | Bonggi | Malaysia | <10K
237 | fau | Fayu | Indonesia | <10K
238 | ilu | Ili’uun | Indonesia | <10K
239 | yet | Yetfa | Indonesia | <10K
240 | dmy | Sowari | Indonesia | <10K
241 | ddw | Dawera-Daweloor | Indonesia | <10K
242 | jhi | Jehai | Malaysia | <10K
243 | xmt | Matbat | Indonesia | <10K
244 | beg | Belait | Brunei | <10K
245 | ivb | Ibatan | Philippines | <10K
246 | oia | Oirata | Indonesia | <10K
247 | bkl | Berik | Indonesia | <10K
248 | duo | Dupaninan Agta | Philippines | <10K
249 | kdw | Koneraw | Indonesia | <10K
250 | msf | Mekwei | Indonesia | <10K
251 | nqm | Ndom | Indonesia | <10K
252 | sbg | Moi Lemas | Indonesia | <10K
253 | seu | Serui-Laut | Indonesia | <10K
254 | tve | Te’un | Indonesia | <10K
255 | tzn | Tugun | Indonesia | <10K
256 | wng | Wanggom | Indonesia | <10K
257 | bnj | Bangon | Philippines | <10K
258 | snv | Sa’ban | Indonesia, Malaysia | <10K
259 | bdw | Baham | Indonesia | <10K
260 | ran | Riantana | Indonesia | <10K
261 | rnn | Roon | Indonesia | <10K
262 | szp | Suabo | Indonesia | <10K
263 | zbe | East Berawan | Malaysia | <10K
264 | scb | Chut | Laos, Vietnam | <10K
265 | tvm | Tela-Masbuar | Indonesia | <10K
266 | udj | Ujir | Indonesia | <10K
267 | agy | Southern Alta | Philippines | <10K
268 | air | Airoran | Indonesia | <10K
269 | aqm | Atohwaim | Indonesia | <10K
270 | asi | Buruwai | Indonesia | <10K
271 | att | Pamplona Atta | Philippines | <10K
272 | bcd | North Babar | Indonesia | <10K
273 | bnf | Masiwang | Indonesia | <10K
274 | btq | Batek | Malaysia | <10K
275 | cth | Thaiphum Chin | Myanmar | <10K
276 | dem | Dem | Indonesia | <10K
277 | dmg | Upper Kinabatangan | Malaysia | <10K
278 | dnu | Danau | Myanmar | <10K
279 | etz | Semimi | Indonesia | <10K
280 | jbj | Arandai | Indonesia | <10K
281 | kbv | Dla | Indonesia | <10K
282 | kpu | Kafoa | Indonesia | <10K
283 | kvy | Yintale | Myanmar | <10K
284 | msg | Moraid | Indonesia | <10K
285 | nks | North Asmat | Indonesia | <10K
286 | pnx | Phong-Kniang | Laos | <10K
287 | sob | Sobei | Indonesia | <10K
288 | wgo | Ambel | Indonesia | <10K
289 | wno | Wano | Indonesia | <10K
290 | xse | Sempan | Indonesia | <10K
291 | zbw | West Berawan | Malaysia | <10K
Not in SEACrowd
292 | rbk | Northern Bontok | Philippines | <10K
293 | kvt | Lahta | Myanmar | <10K
294 | lbg | Laopang | Laos | <10K
295 | stu | Samtao | Myanmar | <10K
296 | kxk | Zayein | Myanmar | <10K
297 | iti | Inlaud Itneg | Philippines | <10K
298 | nqq | Chen-Kayu Naga | Myanmar | <10K
299 | pnc | Pannei | Indonesia | <10K
300 | zkn | Kanan | Myanmar | <10K
301 | mlz | Malaynon | Philippines | <10K
302 | khf | Khuen | Laos | <10K
303 | kkx | Kohin | Indonesia | <10K
304 | lmj | West Lembata | Indonesia | <10K
305 | dkr | Kuijau | Malaysia | <10K
306 | ebc | Beginci | Indonesia | <10K
307 | mtw | Southern Binukidnon | Philippines | <10K
308 | mqk | Rajah Kabunsuwan Manobo | Philippines | <10K
309 | csx | Cambodian Sign Language | Cambodia | <10K
310 | tis | Masadiit Itneg | Philippines | <10K
311 | csj | Songlai Chin | Myanmar | <10K
312 | mqc | Mangole | Indonesia | <10K
313 | bpz | Bilba | Indonesia | <10K
314 | lmf | South Lembata | Indonesia | <10K
315 | wha | Sou Upaa | Indonesia | <10K
316 | lkc | Kucong | Vietnam | <10K
317 | mqa | Maba | Indonesia | <10K
318 | lcq | Luhu | Indonesia | <10K
319 | mjb | Makalero | East Timor | <10K

Table 41: (4/6) SEA indigenous languages with <10K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
Not in SEACrowd
320 | krv | Kavet | Cambodia | <10K
321 | cey | Ekai Chin | Myanmar | <10K
322 | kjt | Phrae Pwo Karen | Thailand | <10K
323 | kuk | Kepo’ | Indonesia | <10K
324 | put | Putoh | Indonesia | <10K
325 | rjg | Rajong | Indonesia | <10K
326 | sjb | Sajau Basap | Indonesia | <10K
327 | tkz | Takua | Vietnam | <10K
328 | amv | Ambelau | Indonesia | <10K
329 | wlh | Welaun | East Timor, Indonesia | <10K
330 | plz | Paluan Murut | Malaysia | <10K
331 | jkp | Paku Karen | Myanmar | <10K
332 | adb | Atauran | East Timor | <10K
333 | nea | Eastern Ngad’a | Indonesia | <10K
334 | ntd | Northern Tidung | Malaysia | <10K
335 | phh | Phula | Vietnam | <10K
336 | reb | Rembong | Indonesia | <10K
337 | skx | Seko Padang | Indonesia | <10K
338 | swu | Suwawa | Indonesia | <10K
339 | tgr | Tareng | Laos | <10K
340 | weu | Rawngtu Chin | Myanmar | <10K
341 | sau | Saleman | Indonesia | <10K
342 | thi | Tai Long | Laos | <10K
343 | low | Tampias Lobu | Malaysia | <10K
344 | npg | Ponyo-Gongwang Naga | Myanmar | <10K
345 | ukk | Muak Sa-aak | Myanmar | <10K
346 | tlq | Tai Loi | Laos, Myanmar | <10K
347 | hkn | Mel-Khaonh | Cambodia | <10K
348 | jkm | Mobwa Karen | Myanmar | <10K
349 | lmq | Lamatuka | Indonesia | <10K
350 | lvu | Levuka | Indonesia | <10K
351 | lwe | Lewoeleng | Indonesia | <10K
352 | rtc | Rungtu Chin | Myanmar | <10K
353 | ruu | Lanas Lobu | Malaysia | <10K
354 | tiu | Adasen | Philippines | <10K
355 | umn | Paungnyuan Naga | Myanmar | <10K
356 | lhh | Laha | Indonesia | <10K
357 | bjx | Vanaw Kalinga | Philippines | <10K
358 | bvt | Bati | Indonesia | <10K
359 | kqv | Okolod | Indonesia, Malaysia | <10K
360 | xkk | Kachok | Cambodia | <10K
361 | iwk | I-wak | Philippines | <10K
362 | lka | Lakalei | East Timor | <10K
363 | bzn | Boano | Indonesia | <10K
364 | sbr | Sembakung Murut | Indonesia, Malaysia | <10K
365 | bfg | Busang Kayan | Indonesia | <10K
366 | hap | Hupla | Indonesia | <10K
367 | kxi | Keningau Murut | Malaysia | <10K
368 | llq | Lolak | Indonesia | <10K
369 | roc | Cacgia Roglai | Vietnam | <10K
370 | sls | Singapore Sign Language | Singapore | <10K
371 | ste | Liana-Seti | Indonesia | <10K
372 | ulu | Uma’ Lung | Indonesia | <10K
373 | wli | Waioli | Indonesia | <10K
374 | wrx | Wae Rana | Indonesia | <10K
375 | xhv | Khua | Laos, Vietnam | <10K
376 | tdy | Tadyawan | Philippines | <10K
377 | zbt | Batui | Indonesia | <10K
378 | sws | Seluwasan | Indonesia | <10K
379 | pni | Aoheng | Indonesia | <10K
380 | tuj | Tugutil | Indonesia | <10K
381 | nps | Nipsan | Indonesia | <10K
382 | uan | Kuan | Laos | <10K
383 | vbk | Southwestern Bontok | Philippines | <10K
384 | dmv | Dumpas | Malaysia | <10K
385 | xko | Kiorr | Laos | <10K
386 | kve | Kalabakan Murut | Malaysia | <10K
387 | mcm | Malaccan Portuguese Creole | Malaysia | <10K
388 | ltu | Latu | Indonesia | <10K
389 | gef | Gerai | Indonesia | <10K
390 | cnc | Côông | Vietnam | <10K
391 | bpo | Anasi | Indonesia | <10K
392 | hld | Halang Doan | Laos, Vietnam | <10K
393 | nxk | Kokak Naga | Myanmar | <10K
394 | puj | Punan Tubu | Indonesia | <10K
395 | xkn | Kayan River Kayan | Indonesia | <10K
396 | ycp | Chepya | Laos | <10K
397 | lcs | Lisabata-Nuniali | Indonesia | <10K
398 | haf | Haiphong Sign Language | Vietnam | <10K
399 | slt | Sila | Laos, Vietnam | <10K

Table 42: (5/6) SEA indigenous languages with <10K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
Not in SEACrowd
400 | kvh | Komodo | Indonesia | <10K
401 | apf | Pahanan Agta | Philippines | <10K
402 | bzb | Andio | Indonesia | <10K
403 | jal | Yalahatan | Indonesia | <10K
404 | mvr | Marau | Indonesia | <10K
405 | agz | Mt. Iriga Agta | Philippines | <10K
406 | dkk | Dakka | Indonesia | <10K
407 | gak | Gamkonora | Indonesia | <10K
408 | kmd | Majukayang Kalinga | Philippines | <10K
409 | mqp | Manipa | Indonesia | <10K
410 | pzn | Jejara Naga | Myanmar | <10K
411 | xkd | Mendalam Kayan | Indonesia | <10K
412 | xay | Kayan Mahakam | Indonesia | <10K
413 | xky | Uma’ Lasan | Indonesia, Malaysia | <10K
414 | mqq | Minokok | Malaysia | <10K
415 | neo | Ná-Meo | Vietnam | <10K
416 | tln | Talondo’ | Indonesia | <10K
417 | bqy | Kata Kolok | Indonesia | <10K
418 | mxr | Murik | Malaysia | <10K
419 | nty | Mantsi | Vietnam | <10K
420 | tev | Teor | Indonesia | <10K
421 | ttp | Tombelala | Indonesia | <10K
422 | ayt | Magbukun Ayta | Philippines | <10K
423 | ckn | Kaang Chin | Myanmar | <10K
424 | cno | Con | Laos | <10K
425 | goq | Gorap | Indonesia | <10K
426 | hov | Hovongan | Indonesia | <10K
427 | lpn | Long Phuri Naga | Myanmar | <10K
428 | nlq | Lao Naga | Myanmar | <10K
429 | nqy | Akyaung Ari Naga | Myanmar | <10K
430 | nuo | Ngoaun | Laos, Vietnam | <10K
431 | psg | Penang Sign Language | Malaysia | <10K
432 | ues | Kioko | Indonesia | <10K

Table 43: (6/6) SEA indigenous languages with <10K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
1 | sow | Sowanda | Indonesia | <1K
2 | duv | Duvle | Indonesia | <1K
3 | hmu | Hamap | Indonesia | <1K
4 | ktt | Ketum | Indonesia | <1K
5 | mpz | Mpi | Thailand | <1K
6 | tvw | Sedoa | Indonesia | <1K
7 | syo | Su’ung | Cambodia | <1K
8 | mgk | Mawes | Indonesia | <1K
9 | mss | West Masela | Indonesia | <1K
10 | dij | Dai | Indonesia | <1K
11 | drn | West Damar | Indonesia | <1K
12 | lji | Laiyolo | Indonesia | <1K
13 | mth | Munggui | Indonesia | <1K
14 | psn | Panasuan | Indonesia | <1K
15 | ret | Reta | Indonesia | <1K
16 | twg | Tereweng | Indonesia | <1K
17 | bpg | Bonggo | Indonesia | <1K
18 | agt | Central Cagayan Agta | Philippines | <1K
19 | kvz | Tsaukambo | Indonesia | <1K
20 | skp | Sekapan | Malaysia | <1K
21 | bsm | Busami | Indonesia | <1K
22 | bzi | Bisu | Thailand | <1K
23 | kzm | Kais | Indonesia | <1K
24 | mhz | Mor | Indonesia | <1K
25 | nkj | Nakai | Indonesia | <1K
26 | pru | Puragi | Indonesia | <1K
27 | skv | Skou | Indonesia | <1K
28 | laq | Qabiao | Vietnam | <1K
29 | ssm | Semnam | Malaysia | <1K
30 | slg | Selungai Murut | Indonesia, Malaysia | <1K
31 | tpf | Tarpia | Indonesia | <1K
32 | vto | Vitou | Indonesia | <1K
33 | wsa | Warembori | Indonesia | <1K
34 | dgc | Casiguran Dumagat Agta | Philippines | <1K
35 | bfe | Betaf | Indonesia | <1K
36 | kgb | Kawe | Indonesia | <1K
37 | kwh | Kowiai | Indonesia | <1K
38 | ppm | Papuma | Indonesia | <1K
39 | tdi | Tomadino | Indonesia | <1K
40 | tmu | Iau | Indonesia | <1K
41 | uka | Kaburi | Indonesia | <1K
42 | bkn | Bukitan | Indonesia, Malaysia | <1K
43 | imr | Imroing | Indonesia | <1K
44 | tgq | Tring | Malaysia | <1K
45 | tlk | Taloki | Indonesia | <1K
46 | ert | Eritai | Indonesia | <1K
47 | lpe | Lepki | Indonesia | <1K
48 | vme | East Masela | Indonesia | <1K
49 | mxz | Central Masela | Indonesia | <1K
50 | aos | Taikat | Indonesia | <1K
51 | cog | Chong | Thailand | <1K
52 | dpp | Papar | Malaysia | <1K
53 | jet | Manem | Indonesia | <1K
54 | kag | Kajaman | Malaysia | <1K
55 | kgi | Selangor Sign Language | Malaysia | <1K
56 | kly | Kalao | Indonesia | <1K
57 | knd | Konda | Indonesia | <1K
58 | kuc | Kwinsu | Indonesia | <1K
59 | lvi | Lavi | Laos | <1K
60 | nbn | Kuri | Indonesia | <1K
61 | ner | Yahadian | Indonesia | <1K
62 | oni | Onin | Indonesia | <1K
63 | orz | Ormu | Indonesia | <1K
64 | pkt | Maleng | Laos, Vietnam | <1K
65 | rth | Ratahan | Indonesia | <1K
66 | sbt | Kimki | Indonesia | <1K
67 | tcm | Tanahmerah | Indonesia | <1K
68 | trt | Tunggare | Indonesia | <1K
69 | wtw | Wotu | Indonesia | <1K
70 | xkq | Koroni | Indonesia | <1K
71 | cwg | Cheq Wong | Malaysia | <1K
72 | bpp | Kaure | Indonesia | <1K
73 | isd | Isnag | Philippines | <1K
74 | pna | Punan Bah-Biau | Malaysia | <1K
75 | skz | Sekar | Indonesia | <1K
76 | thm | Aheu | Thailand | <1K
77 | toy | Topoiyo | Indonesia | <1K
78 | dbe | Dabe | Indonesia | <1K
79 | bvk | Bukat | Indonesia | <1K
80 | dei | Demisa | Indonesia | <1K

Table 44: (1/3) SEA indigenous languages with <1K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
81 | jel | Yelmek | Indonesia | <1K
82 | nun | Anong | Myanmar | <1K
83 | opk | Kopkaka | Indonesia | <1K
84 | pas | Papasena | Indonesia | <1K
85 | tmj | Samarokena | Indonesia | <1K
86 | urn | Uruangnirin | Indonesia | <1K
87 | xau | Kauwera | Indonesia | <1K
88 | kdy | Keijar | Indonesia | <1K
89 | auu | Auye | Indonesia | <1K
90 | auw | Awyi | Indonesia | <1K
91 | flh | Foau | Indonesia | <1K
92 | gop | Yeretuar | Indonesia | <1K
93 | jau | Yaur | Indonesia | <1K
94 | lhn | Lahanan | Malaysia | <1K
95 | pee | Taje | Indonesia | <1K
96 | phq | Phana’ | Laos | <1K
97 | tnz | Ten’edn | Malaysia, Thailand | <1K
98 | wru | Waru | Indonesia | <1K
99 | sve | Serili | Indonesia | <1K
100 | bgv | Warkay-Bipim | Indonesia | <1K
101 | bhc | Biga | Indonesia | <1K
102 | bqb | Bagusa | Indonesia | <1K
103 | bsa | Abinomn | Indonesia | <1K
104 | ccm | Malaccan Malay Creole | Malaysia | <1K
105 | giq | Green Gelao | Vietnam | <1K
106 | kja | Mlap | Indonesia | <1K
107 | kzv | Komyandaret | Indonesia | <1K
108 | mrf | Elseng | Indonesia | <1K
109 | swr | Saweru | Indonesia | <1K
110 | tad | Tause | Indonesia | <1K
111 | tbp | Diebroud | Indonesia | <1K
112 | tmo | Temoq | Malaysia | <1K
113 | tyh | O’du | Laos, Vietnam | <1K
114 | wuy | Wauyai | Indonesia | <1K
115 | xwr | Kwerba Mamberamo | Indonesia | <1K
116 | rmh | Murkim | Indonesia | <1K
117 | tml | Tamnim Citak | Indonesia | <1K
118 | wet | Perai | Indonesia | <1K
119 | bqq | Biritai | Indonesia | <1K
120 | brs | Baras | Indonesia | <1K
121 | bzu | Burmeso | Indonesia | <1K
122 | emw | Emplawas | Indonesia | <1K
123 | kiq | Kosare | Indonesia | <1K
124 | kiy | Kirikiri | Indonesia | <1K
125 | kns | Kensiu | Malaysia, Thailand | <1K
126 | lcc | Legenyem | Indonesia | <1K
127 | mso | Mombum | Indonesia | <1K
128 | mvx | Meoswar | Indonesia | <1K
129 | sao | Sause | Indonesia | <1K
130 | snu | Viid | Indonesia | <1K
131 | tlg | Tofanma | Indonesia | <1K
132 | kgv | Karas | Indonesia | <1K
133 | lnh | Lanoh | Malaysia | <1K
134 | asz | As | Indonesia | <1K
135 | kbi | Kaptiau | Indonesia | <1K
136 | msl | Molof | Indonesia | <1K
137 | wfg | Zorop | Indonesia | <1K
138 | dmu | Tebi | Indonesia | <1K
139 | llk | Lelak | Malaysia | <1K
140 | tcq | Kaiy | Indonesia | <1K
141 | aqn | Northern Alta | Philippines | <1K
142 | bnv | Beneraf | Indonesia | <1K
143 | enc | En | Vietnam | <1K
144 | erw | Erokwanas | Indonesia | <1K
145 | jbr | Jofotek-Bromnya | Indonesia | <1K
146 | khh | Kehu | Indonesia | <1K
147 | khp | Kapauri | Indonesia | <1K
148 | kxn | Kanowit-Tanjong Melanau | Malaysia | <1K
149 | mmb | Momina | Indonesia | <1K
150 | nec | Nedebang | Indonesia | <1K
151 | nyl | Nyeu | Thailand | <1K
152 | rac | Rasawa | Indonesia | <1K
153 | tnu | Tai Khang | Laos | <1K
154 | wai | Wares | Indonesia | <1K
155 | yki | Yoke | Indonesia | <1K
156 | bed | Bedoanas | Indonesia | <1K
157 | mzt | Mintil | Malaysia | <1K
158 | agf | Arguni | Indonesia | <1K
159 | apx | Aputai | Indonesia | <1K
160 | kcd | Ngkâlmpw Kanum | Indonesia | <1K

Table 45: (2/3) SEA indigenous languages with <1K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
161 | ugo | Ugong | Thailand | <1K
162 | wbe | Waritai | Indonesia | <1K
163 | mra | Mlabri | Laos, Thailand | <1K
164 | afz | Obokuitai | Indonesia | <1K
165 | mgf | Maklew | Indonesia | <1K
166 | ttn | Towei | Indonesia | <1K
167 | knq | Kintaq | Malaysia | <1K
168 | ulf | Usku | Indonesia | <1K
169 | awh | Awbono | Indonesia | <1K
170 | bti | Burate | Indonesia | <1K
171 | byl | Bayono | Indonesia | <1K
172 | diy | Diuwe | Indonesia | <1K
173 | kpi | Kofei | Indonesia | <1K
174 | krz | Sota Kanum | Indonesia | <1K
175 | kwr | Kwer | Indonesia | <1K
176 | tfo | Tefaro | Indonesia | <1K
177 | tkx | Tangko | Indonesia | <1K
178 | tti | Tobati | Indonesia | <1K
Not in SEACrowd
179 | lcd | Lola | Indonesia | <1K
180 | ors | Orang Seletar | Malaysia | <1K
181 | kpd | Koba | Indonesia | <1K
182 | trx | Tringgus-Sembaan Bidayuh | Malaysia | <1K
183 | kqt | Klias River Kadazan | Malaysia | <1K
184 | atp | Pudtol Atta | Philippines | <1K
185 | tcp | Tawr Chin | Myanmar | <1K
186 | kyd | Karey | Indonesia | <1K
187 | pyy | Pyen | Myanmar | <1K
188 | ttw | Long Wat | Malaysia | <1K
189 | xmx | Salawati | Indonesia | <1K
190 | ymn | Sunum | Indonesia | <1K
191 | wkd | Mo | Indonesia | <1K
192 | abf | Abai Sungai | Malaysia | <1K
193 | esy | Eskayan | Philippines | <1K
194 | kzb | Kaibobo | Indonesia | <1K
195 | njs | Nisa | Indonesia | <1K
196 | nni | North Nuaulu | Indonesia | <1K
197 | whu | Wahau Kayan | Indonesia | <1K
198 | xke | Kereho | Indonesia | <1K
199 | lce | Sekak | Indonesia | <1K
200 | sdx | Sibu Melanau | Malaysia | <1K
201 | bfk | Ban Khor Sign Language | Thailand | <1K
202 | kax | Kao | Indonesia | <1K
203 | srk | Serudung Murut | Malaysia | <1K
204 | pud | Punan Aput | Indonesia | <1K
205 | bgy | Benggoi | Indonesia | <1K
206 | kzd | Kadai | Indonesia | <1K
207 | kvp | Kompane | Indonesia | <1K
208 | auq | Anus | Indonesia | <1K
209 | azt | Faire Atta | Philippines | <1K
210 | hud | Huaulu | Indonesia | <1K
211 | lgh | Laghuu | Vietnam | <1K
212 | tip | Trimuris | Indonesia | <1K
213 | tyj | Tai Yo | Laos, Vietnam | <1K
214 | tys | Tày Sa Pa | Vietnam | <1K
215 | mqi | Mariri | Indonesia | <1K
216 | pdn | Fedan | Indonesia | <1K
217 | mnq | Minriq | Malaysia | <1K
218 | daz | Dao | Indonesia | <1K
219 | gnq | Gana | Malaysia | <1K
220 | lrn | Lorang | Indonesia | <1K
221 | bsu | Bahonsuai | Indonesia | <1K
222 | puc | Punan Merap | Indonesia | <1K
223 | rmx | Romam | Vietnam | <1K
224 | tyl | Thu Lao | Vietnam | <1K
225 | yrs | Yarsun | Indonesia | <1K
226 | atl | Mt. Iraya Agta | Philippines | <1K
227 | puf | Punan Merah | Indonesia | <1K
228 | umi | Ukit | Malaysia | <1K
229 | jvd | Javindo | Indonesia | <1K
230 | srt | Sauri | Indonesia | <1K

Table 46: (3/3) SEA indigenous languages with <1K speakers.

No. | ISO 639-3 | Language | Region(s) | Population
---|---|---|---|---
In SEACrowd
1 | mnu | Mer | Indonesia | <100
2 | itx | Itik | Indonesia | <100
3 | kxq | Smärky Kanum | Indonesia | <100
4 | lix | Liabuku | Indonesia | <100
5 | awr | Awera | Indonesia | <100
6 | bdx | Budong-Budong | Indonesia | <100
7 | ire | Yeresiam | Indonesia | <100
8 | tds | Doutai | Indonesia | <100
9 | mrx | Dineor | Indonesia | <100
10 | amq | Amahai | Indonesia | <100
11 | kzu | Kayupulau | Indonesia | <100
12 | mok | Morori | Indonesia | <100
13 | plh | Paulohi | Indonesia | <100
14 | sgu | Salas | Indonesia | <100
15 | aip | Burumakok | Indonesia | <100
16 | dbn | Duriankere | Indonesia | <100
17 | dul | Inagta Alabat | Philippines | <100
18 | moq | Mor | Indonesia | <100
19 | naa | Namla | Indonesia | <100
20 | mvs | Massep | Indonesia | <100
21 | aem | Arem | Laos, Vietnam | <100
22 | mqr | Mander | Indonesia | <100
23 | xkw | Kembra | Indonesia | <100
24 | kkb | Kwerisa | Indonesia | <100
25 | atz | Arta | Philippines | <100
26 | ibh | Bih | Vietnam | <100
27 | khd | Bädi Kanum | Indonesia | <100
28 | nul | Nusa Laut | Indonesia | <100
29 | scq | Chung | Cambodia | <100
30 | mqt | Mok | Myanmar, Thailand | <10
31 | btj | Bacanese Malay | Indonesia | <10
32 | wor | Woria | Indonesia | <10
33 | spi | Saponi | Indonesia | <10
34 | dsn | Dusner | Indonesia | <10
35 | lgi | Lengilu | Indonesia | <10
36 | btn | Ratagnon | Philippines | <10
37 | tni | Tandia | Indonesia | <10
38 | huw | Hukumina | Indonesia | <10
39 | kzl | Kayeli | Indonesia | <10
40 | sxm | Samre | Cambodia, Thailand | <10
41 | hpo | Hpon | Myanmar | <10
42 | mpy | Mapia | Indonesia | <10
43 | nil | Nila | Indonesia | <10
44 | sbo | Sabüm | Malaysia | <10
45 | srw | Serua | Indonesia | <10
46 | tas | Tay Boi | Vietnam | <10
47 | xbn | Kenaboi | Malaysia | <10
48 | xxt | Tambora | Indonesia | <10
Not in SEACrowd
49 | orn | Orang Kanaq | Malaysia | <100
50 | lva | Makuva | East Timor | <100
51 | spg | Sihan | Malaysia | <100
52 | ibu | Ibu | Indonesia | <100
53 | pnm | Punan Batu | Malaysia | <100
54 | csd | Chiangmai Sign Language | Thailand | <100
55 | ays | Sorsogon Ayta | Philippines | <100
56 | lio | Liki | Indonesia | <100
57 | pey | Petjo | Indonesia | <100
58 | hti | Hoti | Indonesia | <100
59 | huk | Hulung | Indonesia | <100
60 | ism | Masimasi | Indonesia | <100
61 | kzx | Kamarian | Indonesia | <100
62 | pns | Ponosakan | Indonesia | <100
63 | agk | Katubung Agta | Philippines | <10
64 | nae | Naka’ela | Indonesia | <10
65 | atm | Ata | Philippines | <10
66 | ihb | Iha Based Pidgin | Indonesia | <10
67 | tvy | Timor Pidgin | East Timor | <10
68 | duy | Dicamay Agta | Philippines | <10
69 | dyg | Villa Viciosa Agta | Philippines | <10
70 | lox | Loun | Indonesia | <10
71 | onx | Onin Based Pidgin | Indonesia | <10
72 | tcl | Taman | Myanmar | <10
73 | vms | Moksela | Indonesia | <10
74 | wea | Wewaw | Myanmar | <10

Table 47: SEA indigenous languages with <100 speakers.