  As voice assistants cement their place in our technologically advanced
society, there remains a need to cater to the diverse linguistic landscape,
including colloquial forms of low-resource languages. Our study introduces the
first-ever comprehensive dataset for intent detection and slot filling in
formal Bangla, colloquial Bangla, and Sylheti languages, totaling 984 samples
across 10 unique intents. Our analysis reveals the robustness of large language
models for tackling downstream tasks with inadequate data. The GPT-3.5 model
achieves an impressive F1 score of 0.94 in intent detection and 0.51 in slot
filling for colloquial Bangla.
