Papers by Sze Jue Yang
MalayMMLU: A Multitask Benchmark for the Low-Resource Malay Language (2024.findings-emnlp)
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Soon Poh, Sze Jue Yang, Jeraelyn Tan, Lawrence Chieng, Jia Tan, Zhenyu Yu, Foong Mun, Chee Seng Chan
| Challenge: | Large Language Models (LLMs) and Large Vision Language Model (LVLMs) exhibit advanced proficiency in language reasoning and comprehension across a wide array of languages. |
| Approach: | They propose to use a multitask language understanding benchmark specifically designed for the Malay language to assess their proficiency. |
| Outcome: | The proposed model performs well in well-resourced languages, but in low-resource languages such as Bahasa Melayu, they are less studied due to a lack of studies and benchmarks. |
Banking Done Right: Redefining Retail Banking with Language-Centric AI (2025.emnlp-industry)
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Xin Jie Chua, Jeraelyn Ming Li Tan, Jia Xuan Tan, Soon Chang Poh, Yi Xian Goh, Debbie Hui Tian Choong, Foong Chee Mun, Sze Jue Yang, Chee Seng Chan
| Challenge: | This is the first global regulator-approved deployment where conversational AI functions as the primary banking interface. |
| Approach: | They propose a framework that powers a conversational AI framework that is powered by a closed-source LLM developed internally and replaces rigid multi-screen workflows with a single dialogue orchestrated by four LLM-powered agents. |
| Outcome: | The proposed framework replaces multi-screen workflows with a single dialogue orchestrated by four LLM-powered agents (Guardrails, Intent, Payment, and FAQ). |