80 citations · 96 across the 34 of their papers we have counts for
38 papers
CoASFL: Coupled Consistency Optimization for Asynchronous Split Federated Learning
Junhao Cheng, Yunming Liao, Hongli Xu +2
Split Federated Learning (SFL) has emerged as a promising paradigm for training large-scale deep models on resource-constrained edge devices. Although asynchronous SFL alleviates t…
HyperBrowseComp: A Multilingual and Multimodal Stress Test for Web-Browsing Agents
Alham Fikri Aji, Faiz Rizki Ramadhan, Zayd M. K. Zuhri +14
We introduce HyperBrowseComp, a multilingual and multimodal browsing benchmark comprising 423 manually authored and human-validated questions across 13 languages, written by native…
Zero-shot Dependency Parsing with Unsupervised Cross-Lingual Bootstrapping
Lalita Lowphansirikul, Attapol Rutherford, Jian Gang Ngui +2
Pre-trained language models (PLMs) with encoder-based architectures have shown impressive capabilities in zero-shot cross-lingual transfer for various language understanding tasks.…
SEA-CLIP-Tiny: Efficient Multilingual Text-Vision Embedding for Southeast Asian Languages
Puja Ahmad Habibi, Faiz Assabil Firdaus, Ashvanth S +3
Multilingual text-vision embedding models are essential for cross-lingual image-text retrieval, but Southeast Asian languages remain poorly supported due to the region's linguistic…
Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpreter
Patomporn Payoungkhamdee, Napat Laosaengpha, Jenta Wonglertsakul +8
Reasoning with a Code Interpreter (CI) has emerged as an effective paradigm for enhancing the reasoning capabilities of large language models (LLMs) through executable computation…
DuDi: Dual-Signal Distillation with Cross-Lingual Verbalizer
Patomporn Payoungkhamdee, Tinnakit Udsa, Jian Gang Ngui +3
Small language models (SLMs) are efficient and scalable, but their multilingual capabilities degrade severely at sub-billion scales, especially for Southeast Asian (SEA) languages.…
SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understanding
Peerawat Chomphooyod, Jian Gang Ngui, Yosephine Susanto +5
Frontier LLMs perform well in Western contexts, but remain poorly tested on underrepresented cultures such as those in Southeast Asia (SEA). Existing NLI benchmarks are largely Wes…
SEA-LION-Embedding: Open and Reproducible Text Embeddings for Southeast Asia
Peerat Limkonchotiwat, Raymond Ng, Sarana Nutanong +1
Text embeddings are fundamental to many downstream applications, making robustness important for real-world NLP. However, most recent state-of-the-art embedding models are not repr…
Anthropogenic Regional Adaptation in Multimodal Vision-Language Model
Samuel Cahyawijaya, Peerat Limkonchotiwat, Tack Hwa Wong +45
While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is still no dedic…
SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia
Panuthep Tasawong, Jian Gang Ngui, Alham Fikri Aji +2
Culturally aware safeguards are crucial for AI alignment in real-world settings, where safety extends beyond common sense and encompasses diverse local values, norms, and region-sp…
BURMESE-SAN: Burmese NLP Benchmark for Evaluating Large Language Models
Thura Aung, Jann Railey Montalan, Jian Gang Ngui +1
We introduce BURMESE-SAN, the first holistic benchmark that systematically evaluates large language models (LLMs) for Burmese across three core NLP competencies: understanding (NLU…
MultiLexNorm++: A Unified Benchmark and a Generative Model for Lexical Normalization for Asian Languages
Weerayut Buaphet, Thanh-Nhi Nguyen, Risa Kondo +8
Social media data has been of interest to Natural Language Processing (NLP) practitioners for over a decade, because of its richness in information, but also challenges for automat…
SEA-SafeguardBench: Evaluating AI Safety in SEA Languages and Cultures
Panuthep Tasawong, Jian Gang Ngui, Alham Fikri Aji +2
Safeguard models help large language models (LLMs) detect and block harmful content, but most evaluations remain English-centric and overlook linguistic and cultural diversity. Exi…
When Better Teachers Don't Make Better Students: Revisiting Knowledge Distillation for CLIP Models in VQA
Pume Tuchinda, Parinthapat Pengpun, Romrawin Chumpu +3
Vision-language models (VLMs) have achieved remarkable success across multimodal tasks, yet their substantial computational demands hinder efficient deployment. Knowledge distillat…
Distilling Multilingual Vision-Language Models: When Smaller Models Stay Multilingual
Sukrit Sriratanawilai, Jhayahgrit Thongwat, Romrawin Chumpu +3
Vision-language models (VLMs) exhibit uneven performance across languages, a problem that is often exacerbated when the model size is reduced. While Knowledge distillation (KD) dem…
WangchanThaiInstruct: An instruction-following Dataset for Culture-Aware, Multitask, and Multi-domain Evaluation in Thai
Peerat Limkonchotiwat, Pume Tuchinda, Lalita Lowphansirikul +5
Large language models excel at instruction-following in English, but their performance in low-resource languages like Thai remains underexplored. Existing benchmarks often rely on…
SEADialogues: A Multilingual Culturally Grounded Multi-turn Dialogue Dataset on Southeast Asian Languages
Muhammad Dehan Al Kautsar, Aswin Candra, Muhammad Alif Al Hakim +6
Although numerous datasets have been developed to support dialogue systems, most existing chit-chat datasets overlook the cultural nuances inherent in natural human conversations.…
SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?
Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Raymond Ng +9
Multilingual text embeddings are often assumed to encode meaning in a perspective-independent semantic space, yielding stable similarity judgments across tasks and languages. Our r…
Mangosteen: An Open Thai Corpus for Language Model Pretraining
Wannaphong Phatthiyaphaibun, Can Udomcharoenchaikit, Pakpoom Singkorapoom +4
Pre-training data shapes a language model's quality, but raw web text is noisy and demands careful cleaning. Existing large-scale corpora rely on English-centric or language-agnost…
Language Surgery in Multilingual Large Language Models
Joanito Agili Lopo, Muhammad Ravi Shulthan Habibi, Tack Hwa Wong +6
Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across tasks and languages, revolutionizing natural language processing. This paper investigat…
Decom-Renorm-Merge: Model Merging on the Right Space Improves Multitasking
Yuatyong Chaichana, Thanapat Trachu, Peerat Limkonchotiwat +3
In the era of large-scale training, model merging has evolved into a tool for creating multitasking models efficiently. It enables the knowledge of models to be fused, without the…
Assessing Thai Dialect Performance in LLMs with Automatic Benchmarks and Human Evaluation
Peerat Limkonchotiwat, Kanruethai Masuk, Surapon Nonesung +4
Large language models show promising results in various NLP tasks. Despite these successes, the robustness and consistency of LLMs in underrepresented languages remain largely unex…
SEA-LION: Southeast Asian Languages in One Network
Raymond Ng, Thanh Ngan Nguyen, Yuli Huang +28
Recently, Large Language Models (LLMs) have dominated much of the artificial intelligence scene with their ability to process and generate natural languages. However, the majority…
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz +89
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often resu…