Thursday 27 March 2025
The Southeast Asian region is home to a diverse array of languages, with over 1,000 spoken across the countries that make up this vast and vibrant area. However, despite their importance, these languages have historically been overlooked in the development of artificial intelligence (AI) language models. That was until the creation of SEA-HELM, a comprehensive evaluation framework designed specifically for Southeast Asian languages.
SEA-HELM is more than just a collection of datasets or a single AI model – it’s a holistic approach to evaluating the capabilities of language models on Southeast Asian languages. The framework consists of five pillars: NLP CLASSICS, LLM-specifics, SEA Linguistics, SEA Culture, and Safety. Each pillar represents a distinct aspect of language evaluation, from basic tasks like sentiment analysis and question answering to more complex tasks like metaphor identification and cultural knowledge assessment.
The NLP CLASSICS pillar focuses on evaluating the performance of language models on established benchmarks, such as sentiment analysis and natural language inference. The LLM-specifics pillar, on the other hand, assesses the ability of language models to perform specific tasks that are relevant to Southeast Asian languages, like generating responses for chatbots or summarizing text.
The SEA Linguistics pillar is designed to evaluate the linguistic features of Southeast Asian languages, such as morphology and syntax. This includes tasks like identifying the correct word order in a sentence or recognizing the nuances of metaphorical language.
The SEA Culture pillar takes into account the cultural context in which these languages are spoken, evaluating the ability of language models to understand and generate culturally relevant text. This includes assessing their ability to recognize cultural references, idioms, and expressions.
Finally, the Safety pillar ensures that language models do not perpetuate harmful or toxic content, a critical concern in today’s digital landscape.
To evaluate the performance of language models on these tasks, SEA-HELM uses a range of datasets and benchmarks. These include sentiment analysis datasets like NusaX and Wisesight, as well as question answering datasets like TyDi QA-GoldP and XQUAD. The framework also incorporates cultural knowledge datasets like Kalahi and cultural reference datasets like LINDSEA.
The results of these evaluations are then used to identify areas where language models need improvement, allowing developers to refine their models and better serve the diverse population of Southeast Asia.
Cite this article: “Advancing Artificial Intelligence in Southeast Asian Languages with SEA-HELM”, The Science Archive, 2025.
Artificial Intelligence, Language Models, Southeast Asian Languages, Evaluation Framework, Natural Language Processing, Nlp Classics, Llm-Specifics, Sea Linguistics, Cultural Context, Safety
