Current evaluations often treat Chinese varieties as monolithic, obscuring failures for regional users. We introduce RegioZH, a 2,607-item multiple-choice benchmark for regional lexical reasoning across Mainland China, Taiwan, Hong Kong, Singapore, and Malaysia. Built by region-specific annotators and expert-validated, it targets localized idioms, slang, code-mixing, politeness conventions, and sociocultural expressions. Zero-shot results on eleven LLMs show large disparities, including a 29.4-point average accuracy gap between Taiwan and Singapore. Persona and structured chain-of-thought prompts for five models yield selective gains, suggesting that some errors stem from retrieval or framing rather than only missing knowledge. Representation, attention, calibration, and surface-feature analyses show a dissociation between marker attention and semantic grounding, overconfident forced-choice errors, and localized lexical density as a consistent difficulty predictor. Qualitative analysis finds terminology misunderstandings, sociocultural inference gaps, and cross-regional semantic divergence. RegioZH demonstrates that apparent Chinese proficiency can mask uneven understanding across regional varieties, motivating intra-language robustness evaluation alongside multilingual generalization.

Multilingual Large Language Models (MLLMs) demonstrate strong generalization across languages, yet they remain prone to hallucinations, especially in low-resource languages, due to training data imbalances. These hallucinations, which include inaccurate or fabricated outputs, are particularly problematic in domain-specific generation tasks (Chataigner et al., 2024). To address this challenge, we propose CCL-XCoT (Curriculum-based Contrastive Learning-based Cross-lingual Chain-of-Thought), a two-stage fine-tuning framework for mitigating hallucination in MLLMs. Our approach first enhances cross-lingual semantic alignment through curriculum-based contrastive learning combined with next-token prediction during continued pre-training. Building on this foundation, we then introduce a cross-lingual Chain-of-Thought (XCoT) prompting strategy during instruction fine-tuning, which guides the model to reason in a high-resource language before generating answers in the target low-resource language. Experimental results show that CCL-XCoT reduces hallucination rates by up to 62% and substantially improves factual knowledge transfer across language pairs, without relying on external retrieval or multi-model ensembles.

The Singapore’s Ministry of Communications and Information (MCI) has officially launched the SG Translate Together (SGTT) web portal on 27 June 2022, with the aim of partnering its citizens to improve translation standards in Singapore. This web portal houses the Singapore Government’s first neural machine translation (MT) engine, known as SG Translate, which was jointly developed by MCI and the Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR). Adapted using localised translation data, SG Translate is able to generate translations that are attuned to Singapore’s context and supports Singapore’s four (4) official languages – English (Singapore), Chinese (Singapore), Bahasa Melayu (Singapore) and Tamil (Singapore). Upon completion of development, MCI allowed all Government agencies to use SG Translate for their daily operations. This presentation will briefly cover the methodologies adopted and showcase SG Translate’s capability to translate content involving local culture, everyday life and government policies and schemes. This presentation will also showcase MCI’s sustainable approach for the continual training of the SG Translate MT engine through citizenry participation.