  The unique capabilities of Large Language Models (LLMs), such as the natural
language text generation ability, position them as strong candidates for
providing explanation for recommendations. However, despite the size of the
LLM, most existing models struggle to produce zero-shot explanations reliably.
To address this issue, we propose a framework called Logic-Scaffolding, that
combines the ideas of aspect-based explanation and chain-of-thought prompting
to generate explanations through intermediate reasoning steps. In this paper,
we share our experience in building the framework and present an interactive
demonstration for exploring our results.
