SEACrowd: A multilingual multimodal data hub and benchmark suite for Southeast Asian languages
A multilingual, multimodal hub with benchmarks for nearly 1,000 Southeast Asian languages across text, image, and audio for improved AI.
Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising the quality of AI models for SEA languages. Evaluating models for SEA languages is challenging due to the scarcity of high-quality datasets, compounded by the dominance of English training data, raising concerns about potential cultural misrepresentation. To address these challenges, we introduce SEACrowd, a collaborative initiative that consolidates a comprehensive resource hub that fills the resource gap by providing standardized corpora in nearly 1,000 SEA languages across three modalities. Through our SEACrowd benchmarks, we assess the quality of AI models on 36 indigenous languages across 13 tasks, offering valuable insights into the current AI landscape in SEA. Furthermore, we propose strategies to facilitate greater AI advancements, maximizing potential utility and resource equity for the future of AI in SEA.
Latest publications
RECAP: Regression evaluation for continual adaptation of prompts
A benchmark that measures continual-learning phenomena at the constraint level for prompt-level adaptation methods.
EMNLPMEMGUARD: Preventing memory contamination in long-term memory-augmented large language models
A type-aware memory framework that preserves functional memory boundaries during memory construction and retrieval.
EMNLPSPARC-RAG: Adaptive Sequential–Parallel Scaling with Context Management for Retrieval-Augmented Generation
A multi-agent framework that coordinates sequential and parallel inference-time scaling.
EMNLP