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An LLM for ASEAN, by ASEAN

A truly collaborative LLM reflecting the region’s diverse linguistic and cultural landscape has yet to emerge.

Satria Mahesya Muhammad (The Jakarta Post)
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Sat, April 5, 2025 Published on Apr. 3, 2025 Published on 2025-04-03T16:48:21+07:00

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An LLM for ASEAN, by ASEAN A photo taken on April 1, 2025, shows the GPT chat logo on a laptop screen (right) next to the logo of Deepseek AI application on a smartphone screen in Frankfurt am Main, western Germany. (AFP/Kirill Kudryavtsev)

O

penAI’s launch of ChatGPT in 2022 sparked global interest in artificial intelligence. This surge includes the development of large language models (LLMs) by not only companies, but also governments, to enhance national competitiveness, ensure data sovereignty and reduce dependence on foreign AI systems.

AI adoption in Southeast Asia is accelerating, with the region’s AI market projected to grow at 27.71 percent annually (CAGR 2025–2030), reaching a market volume of US$30.3 billion.

However, a truly collaborative LLM reflecting the region’s diverse linguistic and cultural landscape has yet to emerge. AI Singapore’s (AISG) Southeast Asian Languages in One Network (SEA-LION) marks a significant step in this.

While SEA-LION benefits from open-source contributions, its funding and technical development are primarily centralized in Singapore.

The ERIA One ASEAN Start-up White Paper 2024 highlights the country’s substantial investment in developing localized LLMs, reportedly totaling S$70 million (US$52.39 million). In contrast, European multilingual LLMs are typically developed through a collective approach involving universities, private-sector partnerships and funding from the European Union.

A truly regional multilingual LLM should be a product of collective collaboration – an LLM for ASEAN, by ASEAN – ensuring inclusive participation from all member states while reflecting shared linguistic and cultural diversity.

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LLMs are advanced AI systems designed to understand and generate human-like responses. Built on neural network architectures, particularly transformers, they are trained using deep learning techniques on vast datasets that include publicly available websites, books and code.

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