Revise the NLU: A Prompting Strategy for Robust Dialogue System

Abstract

The advent of large language models (LLMs), such as GPT 3.5, has demonstrated significant potential, especially when paired with the prompt engineering techniques. However, while this setting excels in zero-shot or few-shot scenario, the direct utilization of LLMs in multi-domain task-oriented dialogue (TOD) systems often falls short compared to smaller task-specific models in standard evaluations. This indicates the need for further exploration on how to harness the power of LLMs effectively to multi-domain TOD systems. This paper addresses the aforementioned challenge by introducing a novel prompting strategy to enhance the robustness of the existing text-based multi-domain TOD systems. This strategy aims to revise the outputs of natural language understanding (NLU) component through a series of prompting steps. By capitalizing on NLU outputs, a simple and straightforward prompt design can be carried out. Experimental results illustrate the benefit of the proposed strategy in improving robustness of the multi-domain TOD system.