Following OpenAI’s public launch of ChatGPT in November 2022, the underpinnings of artificial intelligence (AI) large language models (LLMs) seemed firmly “WIRED”: Western, industrialized, rich, educated and democratic. Everyone assumed that if LLMs spoke a particular language and reflected a particular worldview, it would be a Western one. OpenAI even acknowledged ChatGPT’s skew toward Western views and the English language.
However, even before OpenAI’s US competitors (Google and Anthropic) released their own LLMs the following year, Southeast Asian developers had recognized the need for AI tools that would speak to their own region in its many languages — no small task, given that more than 1,200 languages are spoken there.
Moreover, in a region where distant civilizational memories often collide with contemporary, post-colonial histories, language is profoundly political. Even seemingly monolingual countries belie marked diversity: Cambodians speak about 30 languages; Thais, about 70; and Vietnamese, more than 100. This is also a region where communities mix languages seamlessly, where nonverbal cues speak volumes, and where oral traditions are sometimes more prevalent than textual means of capturing the deep cultural and historical nuances that have been encoded in language.
Not surprisingly, those trying to build truly local AI models for a region with so many under-represented languages have faced many obstacles, from a paucity of high-quality, high-quantity annotated data to a lack of access to the computing power needed to build and train models from scratch. In some cases, the challenges are even more basic, reflecting a shortage of native speakers and standardized orthography or frequent electricity supply disruptions.
Given these constraints, many of the region’s AI developers have settled for fine-tuning established models built by foreign incumbents. This involves taking a pretrained model that has been fed large quantities of data and training it on a smaller dataset for a specific skill or task. Between 2020 to 2023, Southeast Asian language models such as PhoBERT (Vietnamese), IndoBERT (Indonesian), and Typhoon (Thai) were derived from much larger models such as Google’s BERT; Meta’s RoBERTa (later LLaMA), and France’s Mistral. Even the early versions of SeaLLM, a suite of models optimized for regional languages and released by Alibaba’s DAMO Academy, were built on Meta, Mistral and Google’s architecture.
However, last year Alibaba Cloud’s Qwen disrupted this Western dominance, offering Southeast Asia a wider set of options. A Carnegie Endowment for International Peace study found that five of the 21 regional models launched that year were built on Qwen.
Still, just as Southeast Asian developers previously had to account for a latent Western bias in the available foundation models, now they must be mindful of the ideologically filtered perspectives embedded in pretrained Chinese models. Ironically, efforts to localize AI and ensure greater agency for Southeast Asian communities could deepen developers’ dependence on much larger players, at least in the initial stages.
Nonetheless, Southeast Asian developers have begun to address this problem, too. Multiple models, including SEA-LION (a collection of 11 official regional languages), PhoGPT (Vietnamese), and MaLLaM (Malay), have been pretrained from scratch on a large, generic dataset of each particular language. This key step in the machine-learning process would allow these models to be further fine-tuned for specific tasks.
Although SEA-LION continues to rely on Google’s architecture for its pretraining, its use of a regional language dataset has facilitated the development of homegrown models such as Sahabat-AI, which communicates in Indonesian, Sundanese, Javanese, Balinese and Bataknese. Sahabat-AI proudly describes itself as “a testament to Indonesia’s commitment to AI sovereignty.”
However, representing native perspectives also requires a strong base of local knowledge. We cannot faithfully present Southeast Asian perspectives and values without understanding the politics of language, traditional sense-making and historical dynamics.
For example, time and space — widely understood in the modern context to be linear, divisible, and measurable for the purposes of maximizing productivity — are perceived differently in many indigenous communities. Balinese historical writings that defy conventional patterns of chronology might be viewed as myths or legends in Western terms, but they continue to shape how these communities make sense of the world.
Historians of the region have cautioned that applying a Western lens to local texts heightens the risk of misinterpreting indigenous perspectives. From the 18th to the 19th centuries, Indonesia’s colonial administrators frequently read their own understanding of Javanese chronicles into translated reproductions. As a result, many biased British and European observations of Southeast Asians have come to be treated as valid historical accounts, and ethnic categorizations and stereotypes from official documents have been internalized. If AIs are trained on these data, the biases could end up further entrenched.
Data are not knowledge. Since language is inherently social and political — reflecting the relational experiences of those who use it — asserting agency in the age of AI must go beyond the technical sufficiency of models that communicate in local languages. It requires consciously filtering legacy biases, questioning assumptions about identity and rediscovering indigenous knowledge repositories in languages. We cannot project our cultures faithfully through technology if we barely understand them in the first place.
Elina Noor is a senior fellow in the Asia Program at the Carnegie Endowment for International Peace.
Copyright: Project Syndicate
Eating at a breakfast shop the other day, I turned to an old man sitting at the table next to mine. “Hey, did you hear that the Legislative Yuan passed a bill to give everyone NT$10,000 [US$340]?” I said, pointing to a newspaper headline. The old man cursed, then said: “Yeah, the Chinese Nationalist Party [KMT] canceled the NT$100 billion subsidy for Taiwan Power Co and announced they would give everyone NT$10,000 instead. “Nice. Now they are saying that if electricity prices go up, we can just use that cash to pay for it,” he said. “I have no time for drivel like
Young supporters of former Taiwan People’s Party (TPP) chairman Ko Wen-je (柯文哲) were detained for posting the names and photographs of judges and prosecutors believed to be overseeing the Core Pacific City redevelopment corruption case. The supporters should be held responsible for their actions. As for Ko’s successor, TPP Chairman Huang Kuo-chang (黃國昌), he should reflect on whether his own comments are provocative and whether his statements might be misunderstood. Huang needs to apologize to the public and the judiciary. In the article, “Why does sorry seem to be the hardest word?” the late political commentator Nan Fang Shuo (南方朔) wrote
Chinese Minister of Foreign Affairs Wang Yi (王毅) reportedly told the EU’s top diplomat that China does not want Russia to lose in Ukraine, because the US could shift its focus to countering Beijing. Wang made the comment while meeting with EU High Representative for Foreign Affairs and Security Policy Kaja Kallas on July 2 at the 13th China-EU High-Level Strategic Dialogue in Brussels, the South China Morning Post and CNN reported. Although contrary to China’s claim of neutrality in the Russia-Ukraine conflict, such a frank remark suggests Beijing might prefer a protracted war to keep the US from focusing on
There are no obvious connections between the 7-Eleven retail chain in Japan and the Philippines’ national security concerns in the South China Sea. Here is one, one that also takes in Canadian Broadcasting Corp (CBC), the government of Denmark and Taiwanese plastic surgeons on the way. Japan’s 7-Eleven on Friday last week posted on social media an image of uniforms worn by the chain store’s employees in various locations, including Taiwan, the US, Hawaii, Australia, Sweden, Denmark, Cambodia and the Philippines. If this was intended to promote a sense of camaraderie within the 7-Eleven family, it backfired. Taiwan was tagged with the
RSS