Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2023

Abstract

Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, they still possess limitations, such as failing to ask clarifying questions to ambiguous queries or refuse users’ unreasonable requests, both of which are considered as key aspects of a conversational agent’s proactivity. This raises the question of whether LLM-based conversational systems are equipped to handle proactive dialogue problems. In this work, we conduct a comprehensive analysis of LLM-based conversational systems, specifically focusing on three key aspects of proactive dialogues: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of LLMs, we propose the Proactive Chain-of-Thought prompting scheme, which augments LLMs with the goal planning capability over descriptive reasoning chains. Empirical findings are discussed to promote future studies on LLM-based proactive dialogue systems.

Keywords

large language model, conversational system

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Findings of the Association for Computational Linguistics: EMNLP 2023

First Page

10602

Last Page

10621

Identifier

10.18653/v1/2023.findings-emnlp.711

Publisher

Association for Computational Linguistics

City or Country

Singapore

Additional URL

https://doi.org/10.18653/v1/2023.findings-emnlp.711

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