Recently, there has been a heightened interest in building chatbots based on Large Language Models (LLMs) to emulate human-like qualities in multi-turn conversations. Despite having access to commonsense knowledge to better understand the psychological aspects and causality of dialogue context, even these powerful LLMs struggle to achieve the goals of empathy and emotional support. Current commonsense knowledge derived from dialogue contexts is inherently limited and often fails to adequately anticipate the future course of a dialogue. This lack of foresight can mislead LLMs and hinder their ability to provide effective support. In response to this challenge, we present an innovative framework named Sensible and Visionary Commonsense Knowledge (Sibyl). Designed to concentrate on the immediately succeeding dialogue, this paradigm equips LLMs with the capability to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses. Experimental results demonstrate that incorporating our paradigm for acquiring commonsense knowledge into LLMs comprehensively enhances the quality of their responses.

翻译：近期，基于大语言模型构建能够模拟人类多轮对话特质的聊天机器人引起了广泛关注。尽管这些强大的大语言模型能够利用常识知识来更好地理解对话语境的心理层面与因果关系，它们仍难以实现共情与情感支持的目标。当前从对话语境中衍生的常识知识本质上存在局限，往往无法充分预测对话的未来走向。这种前瞻性的缺失可能误导大语言模型，并阻碍其提供有效支持的能力。为应对这一挑战，我们提出了一种名为感知与前瞻性常识知识的创新框架。该范式专注于紧邻的后续对话，使大语言模型能够揭示对话中的隐含需求，旨在激发更具共情力的回应。实验结果表明，将我们获取常识知识的范式融入大语言模型，能全面提升其回应质量。