Biased GPT? Singapore Develops AI Model to ‘Represent’ Southeast Asians

Context: A government-backed initiative in Singapore aims to address the bias in large language models (LLMs) by creating a Southeast Asian LLM named SEA-LION (Southeast Asian Languages in One Network). This model is designed to be inclusive of Southeast Asian data, considering the linguistic and cultural diversity of the region. The initiative has drawn both supporters and critics.

Singapore’s Southeast Asian LLM – SEA-LION:

SEA-LION is the first model in a series developed to cater to Southeast Asian languages, including Bahasa Indonesia, Thai, Vietnamese, and others. Unlike popular LLMs such as Meta’s Llama 2 and Open AI’s GPT-4, which are predominantly trained in English, SEA-LION is trained on data from 11 Southeast Asian languages.

Leslie Teo from AI Singapore emphasizes that SEA-LION is not meant to compete with existing LLMs but to complement them, ensuring better representation for Southeast Asians in the rapidly evolving landscape of generative artificial intelligence.

Addressing Imbalances and Accessibility:

The initiative seeks to make AI technology more accessible to the diverse linguistic communities in Southeast Asia. By developing models trained on local languages, the goal is to empower people in the region to utilize technology without the necessity of being proficient in English.

Global Efforts to Bridge Language Gaps:

Governments and tech firms worldwide are recognizing the need for language models in local languages. Initiatives in India, the United Arab Emirates, China, Japan, and Vietnam are working towards creating models that reflect linguistic diversity, promoting technology self-reliance, privacy, and aligning with national interests.

Benefits and Challenges of SEA-LION:

SEA-LION, being an open-sourced model, offers a more cost-effective and efficient option for businesses, governments, and academia in Southeast Asia. Approximately 13% of its data is sourced from Southeast Asian languages, making it more representative of the region.

However, critics express concerns about potential biases in the data used to train SEA-LION. The challenge lies in verifying and filtering data, especially as the internet contains material generated by other language models.

Potential Risks and Concerns:

Digital and human rights experts worry that region-specific language models may inadvertently perpetuate biased narratives. Depending on the sources of data, these models could unintentionally promote one-sided or incomplete views, risking the omission of crucial socio-political issues.

There are concerns that government-backed models might contribute to a revisionist view of history, potentially undermining democratic values. On the other hand, relying solely on Western LLMs might perpetuate biases associated with cultural values, political beliefs, and social norms from wealthy, liberal, Western democracies.

Conclusion:

The development of SEA-LION reflects ongoing efforts to create more inclusive and region-specific language models. While the initiative aims to address biases, concerns remain about potential unintended consequences and the need for careful curation of training data to ensure fairness and accuracy.