  The recent success of large language models (LLMs) has shown great potential
to develop more powerful conversational recommender systems (CRSs), which rely
on natural language conversations to satisfy user needs. In this paper, we
embark on an investigation into the utilization of ChatGPT for conversational
recommendation, revealing the inadequacy of the existing evaluation protocol.
It might over-emphasize the matching with the ground-truth items or utterances
generated by human annotators, while neglecting the interactive nature of being
a capable CRS. To overcome the limitation, we further propose an interactive
Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user
simulators. Our evaluation approach can simulate various interaction scenarios
between users and systems. Through the experiments on two publicly available
CRS datasets, we demonstrate notable improvements compared to the prevailing
evaluation protocol. Furthermore, we emphasize the evaluation of
explainability, and ChatGPT showcases persuasive explanation generation for its
recommendations. Our study contributes to a deeper comprehension of the
untapped potential of LLMs for CRSs and provides a more flexible and
easy-to-use evaluation framework for future research endeavors. The codes and
data are publicly available at https://github.com/RUCAIBox/iEvaLM-CRS.
