Willy Fitra Hendria

Willy Fitra Hendria

Multimodal learning on video and text has seen significant progress, particularly in tasks like text-to-video retrieval, video-to-text retrieval, and video captioning. However, most existing methods and datasets focus exclusively on English. Despite Indonesian being one of the most widely spoken languages, multimodal research in Indonesian remains under-explored, largely due to the lack of benchmark datasets. To address this gap, we introduce the first public Indonesian video-text dataset by translating the English captions in the MSVD dataset into Indonesian. Using this dataset, we evaluate neural network models which were developed for the English video-text dataset on three tasks, i.e., text-to-video retrieval, video-to-text retrieval, and video captioning. Most existing models rely on feature extractors pretrained on English vision-language datasets, raising concerns about their applicability to Indonesian, given the scarcity of large-scale pretraining resources in the language. We apply a cross-lingual transfer learning approach by leveraging English-pretrained extractors and fine-tuning models on our Indonesian dataset. Experimental results demonstrate that this strategy improves performance across all tasks and metrics. We release our dataset publicly to support future research and hope it will inspire further progress in Indonesian multimodal learning.

Willy Fitra Hendria

Autoregressive inference is typically assumed to scale predictably with decoding length, and key-value (KV) caching is widely regarded as a universally beneficial optimization for accelerating decoding. In this work, we identify unexpected non-monotonic latency behavior in the Apple MPS backend, where latency changes abruptly across nearby decoding configurations. Using transformer models from multiple families (GPT-2, BLOOM, and OPT), we observe latency spikes of up to 21x within specific decoding-budget intervals, followed by recovery at neighboring configurations. Controlled experiments show that these anomalies are not explained by memory pressure or prefill cost, but are instead consistent with backend execution dynamics, while CPU and NVIDIA T4 (CUDA) exhibit smooth monotonic scaling under identical conditions. Our findings highlight the importance of hardware-aware evaluation for autoregressive inference and caution against relying on aggregated decoding-budget benchmarks, as performance can vary discontinuously across nearby configurations.

Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar et al.

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.