Enhancing Large Language Model Induced Task-Oriented Dialogue Systems
Through Look-Forward Motivated Goals
- URL: http://arxiv.org/abs/2309.08949v1
- Date: Sat, 16 Sep 2023 10:56:00 GMT
- Title: Enhancing Large Language Model Induced Task-Oriented Dialogue Systems
Through Look-Forward Motivated Goals
- Authors: Zhiyuan Hu, Yue Feng, Yang Deng, Zekun Li, See-Kiong Ng, Anh Tuan Luu,
Bryan Hooi
- Abstract summary: ProToD approach anticipates the future dialogue actions and incorporates the goal-oriented reward signal to enhance ToD systems.
We present a novel evaluation method that assesses ToD systems based on goal-driven dialogue simulations.
Empirical experiments conducted on the MultiWoZ 2.1 dataset demonstrate that our model can achieve superior performance using only 10% of the data.
- Score: 76.69419538047813
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, the development of large language models (LLMs) has been
significantly enhanced the question answering and dialogue generation, and
makes them become increasingly popular in current practical scenarios. While
unlike the general dialogue system which emphasizes the semantic performance,
the task-oriented dialogue (ToD) systems aim to achieve the dialogue goal
efficiently and successfully in multiple turns. Unfortunately, existing
LLM-induced ToD systems lack the direct reward toward the final goal and do not
take account of the dialogue proactivity that can strengthen the dialogue
efficiency. To fill these gaps, we introduce the ProToD (Proactively
Goal-Driven LLM-Induced ToD) approach, which anticipates the future dialogue
actions and incorporates the goal-oriented reward signal to enhance ToD
systems. Additionally, we present a novel evaluation method that assesses ToD
systems based on goal-driven dialogue simulations. This method allows us to
gauge user satisfaction, system efficiency and successful rate while overcoming
the limitations of current Information and Success metrics. Empirical
experiments conducted on the MultiWoZ 2.1 dataset demonstrate that our model
can achieve superior performance using only 10% of the data compared to
previous end-to-end fully supervised models. This improvement is accompanied by
enhanced user satisfaction and efficiency.
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