  Large language models (LLMs) are increasingly prevalent in conversational
systems due to their advanced understanding and generative capabilities in
general contexts. However, their effectiveness in task-oriented dialogues
(TOD), which requires not only response generation but also effective dialogue
state tracking (DST) within specific tasks and domains, remains less
satisfying. In this work, we propose a novel approach FnCTOD for solving DST
with LLMs through function calling. This method improves zero-shot DST,
allowing adaptation to diverse domains without extensive data collection or
model tuning. Our experimental results demonstrate that our approach achieves
exceptional performance with both modestly sized open-source and also
proprietary LLMs: with in-context prompting it enables various 7B or 13B
parameter models to surpass the previous state-of-the-art (SOTA) achieved by
ChatGPT, and improves ChatGPT's performance beating the SOTA by 5.6% average
joint goal accuracy (JGA). Individual model results for GPT-3.5 and GPT-4 are
boosted by 4.8% and 14%, respectively. We also show that by fine-tuning on a
small collection of diverse task-oriented dialogues, we can equip modestly
sized models, specifically a 13B parameter LLaMA2-Chat model, with
function-calling capabilities and DST performance comparable to ChatGPT while
maintaining their chat capabilities. We have made the code publicly available
at https://github.com/facebookresearch/FnCTOD
