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% Avg. 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
plan to open-source experimental code and model.

通过函数调用，我们提出了一种改进零样本对话状态跟踪的方法，以提高大型语言模型的任务导向对话的效果，并实现了与当前最先进模型的性能相媲美。

通过函数调用将大型语言模型作为零样本对话状态跟踪器

Large Language Models as Zero-shot Dialogue State Tracker through  Function Calling

Recent research on dialogue state tracking (DST) focuses on methods that
allow few- and zero-shot transfer to new domains or schemas. However,
performance gains heavily depend on aggressive data augmentation and
fine-tuning of ever larger language model based architectures. In contrast,
general purpose language models, trained on large amounts of diverse data, hold
the promise of solving any kind of task without task-specific training. We
present preliminary experimental results on the ChatGPT research preview,
showing that ChatGPT achieves state-of-the-art performance in zero-shot DST.
Despite our findings, we argue that properties inherent to general purpose
models limit their ability to replace specialized systems. We further theorize
that the in-context learning capabilities of such models will likely become
powerful tools to support the development of dedicated and dynamic dialogue
state trackers.

我们使用 ChatGPT ，一个通用语言模型，展示了它在零 - shot DST 上达到的最先进的表现，并证明了通用模型在专业系统替代方面的能力受到其属性的限制，但我们认为通用模型的语境学习能力很可能成为支持专门的动态对话状态跟踪器开发的有力工具。