Grab Superapp Says AI Models Struggle to Understand Asian Languages

Grab, the Singapore-based superapp company, highlighted on Monday that it was forced to develop an in-house artificial intelligence (AI) model for internal use. It is a lightweight vision large language model (LLM) that can scan documents and extract information from them. The company said the decision to develop the model was made as both proprietary and open-source models were not good at understanding Southeast Asian languages. The company's statement has raised fresh concerns around the accessibility of frontier models by Google, OpenAI, and Anthropic.

AI Models' Struggle With Non-English Languages

In a blog post detailing the architecture and training process of their in-house vision model, Grab highlighted the shortcomings they experienced when they tried to outsource the technology. “While powerful proprietary Large Language Models (LLMs) were an option, they often fell short in understanding SEA languages, produced errors, hallucinations, and had high latency. On the other hand, open-sourced Vision LLMs were more efficient but not accurate enough for production,” the post mentioned.

AI models' struggle with non-English languages is not a new finding. For years, researchers have pointed it out, and AI players have tried to fix the issue. However, despite gaining basic competence in popular foreign languages such as Hindi, Japanese, Spanish (Latin America and Spain), and Chinese, the models have yet to understand the lexicon enough to differentiate between the nuances. So, they might be useful in general conversations, but for enterprise or research-based needs, the applicability falls short.

For instance, a paper published earlier this year found that even AI models developed by Chinese companies are as bad in Chinese minority languages as are Western models. And the issue persists in both proprietary models from Google, OpenAI, Meta, and Anthropic, as well as in open-source models.

The reason behind this struggle is the lack of readily available, adequate datasets to train the model on these languages. This is one of the reasons major AI companies are partnering with Indian companies and institutions to collect more Indic language datasets. In July, Google teamed up with IIT Bombay to develop Indic language AI speech models. Meta is reportedly paying $55 an hour to contractors to train its models in the Hindi language, and OpenAI has announced a research collaboration with IIT Madras, backed by $500,000 from the ChatGPT maker.

While collecting data this way is expensive, it is still possible to eventually build large enough datasets in prominent Asian and other languages. However, the minority languages, such as the non-scheduled Indian languages, will still be a struggle for these models to gain competence in. And unless they can learn these languages, accessibility and functionality will always be limited.