Sibyl：通过明智且有远见的常识推理增强大型语言模型中的共情对话生成
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference
AI总结：
本文提出Sibyl框架，通过聚焦后续对话的常识推理，增强大型语言模型的共情对话生成能力，实验证明其全面提升了回应质量。
摘要：
近来，基于大型语言模型（LLMs）构建聊天机器人以在多轮对话中模拟人类特质引起了广泛关注。尽管这些强大的LLMs能够获取常识知识以更好地理解对话背景的心理层面和因果关系，但它们仍难以实现共情和情感支持的目标。当前从对话背景中获取的常识知识本质上具有局限性，往往无法充分预见对话的未来走向。这种缺乏前瞻性可能会误导LLMs，阻碍其提供有效支持的能力。为应对这一挑战，我们提出了一种创新框架，名为明智且有远见的常识知识（Sibyl）。该范式旨在聚焦于紧随其后的对话，赋予LLMs揭示对话隐含需求的能力，以激发更具共情性的回应。实验结果表明，将我们获取常识知识的范式融入LLMs，全面提升了其回应质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
