  Conversational systems based on Large Language Models (LLMs), such as
ChatGPT, show exceptional proficiency in context understanding and response
generation. However, despite their impressive capabilities, they still possess
limitations, such as providing randomly-guessed answers to ambiguous queries or
failing to refuse users' requests, both of which are considered 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 aspects of proactive
dialogue systems: 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.
