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.

翻译：大语言模型（LLM）的独特能力，如自然语言文本生成能力，使其成为提供推荐解释的有力候选。然而，尽管LLM规模庞大，现有大多数模型仍难以可靠地生成零样本解释。为解决这一问题，我们提出了一种名为逻辑框架的框架，该框架融合了基于方面的解释与思维链提示的思想，通过中间推理步骤生成解释。在本文中，我们分享了构建该框架的经验，并提供了一个交互式演示，以探索我们的研究成果。