Polite on the Surface, Wrong in Practice: A Curated Dataset for Fixing Honorific Failures in Multilingual Bangla Generation
arXiv:2605.22487v1 Announce Type: new Abstract: Recent advances in Multilingual Large Language Models (MLLMs) have significantly enhanced cross-lingual conversational capabilities, yet modeling culturally nuanced and context-dependent communication remains a critical bottleneck. Specifically, existing state-of-the-art models exhibit a severe pragmatic gap when handling structural variations, regional idioms, and honorific consistencies in low-resource contexts like Bangla. To address this limitation, we introduce a novel, culturally aligned instruction-tuning dataset for \textbf{BangLa Applica
The rapid advancement of MLLMs is revealing their limitations in culturally nuanced communication, particularly in low-resource languages.
This development highlights a critical bottleneck in AI's cross-cultural applicability and signals the growing need for localized, culturally aware AI solutions.
The focus is shifting towards developing domain-specific, culturally aligned datasets to improve AI's pragmatic understanding and honorific consistency in diverse linguistic contexts.
- · AI researchers specializing in NLP and multilingual models
- · Governments and organizations seeking culturally sensitive AI solutions
- · Users of low-resource languages accessing advanced AI capabilities
- · Generic, non-specialized MLLMs
- · Companies that overlook cultural nuance in AI development
Improved performance of MLLMs in handling politeness and honorifics in languages like Bangla.
Increased investment in creating culturally aligned datasets for other low-resource languages, fostering greater linguistic diversity in AI.
Enhanced trust and adoption of AI systems in communities where cultural and linguistic nuances are critical for effective communication.
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