Despite the impressive performance of LLMs on English-based tasks, little is known about their capabilities in specific languages such as Filipino. In this work, we address this gap by introducing FilBench, a Filipino-centric benchmark designed to evaluate LLMs across a diverse set of tasks and capabilities in Filipino, Tagalog, and Cebuano. We carefully curate the tasks in FilBench to reflect the priorities and trends of NLP research in the Philippines such as Cultural Knowledge, Classical NLP, Reading Comprehension, and Generation. By evaluating 27 state-of-the-art LLMs on FilBench, we find that several LLMs suffer from reading comprehension and translation capabilities. Our results indicate that FilBench is challenging, with the best model, GPT-4o, achieving only a score of 72.23%. Moreover, we also find that models trained specifically for Southeast Asian languages tend to underperform on FilBench, with the highest-performing model, SEA-LION v3 70B, achieving only a score of 61.07%. Our work demonstrates the value of curating language-specific LLM benchmarks to aid in driving progress on Filipino NLP and increasing the inclusion of Philippine languages in LLM development.

Social media has emerged as a valueable source for early pandemic detection, as repeated mentions of symptoms by users may signal the onset of an outbreak. However, to be a reliable system, validation through fact-checking and verification against official health records is essential. Without this step, systems risk spreading misinformation to the public. The effectiveness of these systems also depend on their ability to process data in multiple languages, given the multilingual nature of social media data.Yet, many NLP datasets and disease surveillance system remain heavily English-centric, leading to significant performance gaps for low-resource languages.This issue is especially critical in Southeast Asia, where symptom expression may vary culturally and linguistically.Therefore, this study evaluates the symptom detection capabilities of LLMs in social media posts across multiple languages, models, and symptoms to enhance health-related fact-checking. Our results reveal significant language-based discrepancies, with European languages outperforming under-resourced Southeast Asian languages. Furthermore, we identify symptom-specific challenges, particularly in detecting respiratory illnesses such as influenza, which LLMs tend to overpredict.The overestimation or misclassification of symptom mentions can lead to false alarms or public misinformation when deployed in real-world settings. This underscores the importance of symptom detection as a critical first step in medical fact-checking within early outbreak detection systems.

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, through a collaborative movement, we introduce SEACrowd, a comprehensive resource center 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 Southeast Asia.