2025
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SEA-HELM: Southeast Asian Holistic Evaluation of Language Models
Yosephine Susanto
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Adithya Venkatadri Hulagadri
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Jann Railey Montalan
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Jian Gang Ngui
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Xianbin Yong
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Wei Qi Leong
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Hamsawardhini Rengarajan
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Peerat Limkonchotiwat
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Yifan Mai
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William Chandra Tjhi
Findings of the Association for Computational Linguistics: ACL 2025
With the rapid emergence of novel capabilities in Large Language Models (LLMs), the need for rigorous multilingual and multiculturalbenchmarks that are integrated has become more pronounced. Though existing LLM benchmarks are capable of evaluating specificcapabilities of LLMs in English as well as in various mid- to low-resource languages, including those in the Southeast Asian (SEA)region, a comprehensive and culturally representative evaluation suite for the SEA languages has not been developed thus far.Here, we present SEA-HELM, a holistic linguistic and cultural LLM evaluation suite that emphasises SEA languages, comprisingfive core pillars: (1) NLP CLASSICS, (2) LLM-SPECIFICS, (3) SEA LINGUISTICS, (4) SEA CULTURE, (5) SAFETY. SEA-HELMcurrently supports Filipino, Indonesian, Tamil, Thai, and Vietnamese. We also introduce the SEA-HELM leaderboard, which allows users to understand models’ multilingual and multicultural performance in a systematic and user-friendly manner. We make the SEA-HELM evaluation code publicly available.
2024
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Kalahi: A handcrafted, grassroots cultural LLM evaluation suite for Filipino
Jann Railey Montalan
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Jian Gang Ngui
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Wei Qi Leong
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Yosephine Susanto
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Hamsawardhini Rengarajan
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Alham Fikri Aji
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William Chandra Tjhi
Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation
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Aalamaram: A Large-Scale Linguistically Annotated Treebank for the Tamil Language
A M Abirami
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Wei Qi Leong
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Hamsawardhini Rengarajan
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D Anitha
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R Suganya
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Himanshu Singh
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Kengatharaiyer Sarveswaran
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William Chandra Tjhi
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Rajiv Ratn Shah
Proceedings of the 7th Workshop on Indian Language Data: Resources and Evaluation
Tamil is a relatively low-resource language in the field of Natural Language Processing (NLP). Recent years have seen a growth in Tamil NLP datasets in Natural Language Understanding (NLU) or Natural Language Generation (NLG) tasks, but high-quality linguistic resources remain scarce. In order to alleviate this gap in resources, this paper introduces Aalamaram, a treebank with rich linguistic annotations for the Tamil language. It is hitherto the largest publicly available Tamil treebank with almost 10,000 sentences from diverse sources and is annotated for the tasks of Part-of-speech (POS) tagging, Named Entity Recognition (NER), Morphological Parsing and Dependency Parsing. Close attention has also been paid to multi-word segmentation, especially in the context of Tamil clitics. Although the treebank is based largely on the Universal Dependencies (UD) specifications, significant effort has been made to adjust the annotation rules according to the idiosyncrasies and complexities of the Tamil language, thereby providing a valuable resource for linguistic research and NLP developments.