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As voice assistants cement their place in our technologically advancedsociety, there remains a need to cater to the diverse linguistic landscape,including colloquial forms of low-resource languages. Our study introduces thefirst-ever comprehensive dataset for intent detection and slot filling informal Bangla, colloquial Bangla, and Sylheti languages, totaling 984 samplesacross 10 unique intents. Our analysis reveals the robustness of large languagemodels for tackling downstream tasks with inadequate data. The GPT-3.5 modelachieves an impressive F1 score of 0.94 in intent detection and 0.51 in slotfilling for colloquial Bangla.

Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for Bangla and Sylheti