利用大型语言模型的共情反应生成能力支持在线心理健康咨询
Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support
AI总结：
本研究评估五个大型语言模型在心理健康咨询对话中的共情反应能力，发现其显著优于传统系统，为构建共情对话系统提供新方向。
摘要：
大型语言模型（LLMs）在各种信息检索和推理任务中展现出了卓越的性能。这些计算系统驱动着最先进的对话系统，如ChatGPT和Bard。它们也在满足日益增长的心理健康护理需求方面具有巨大潜力，尽管这一领域相对尚未被充分探索。因此，本研究旨在考察LLMs在模拟心理健康咨询场景的对话中生成共情反应的能力。我们选取了五个LLM：生成式预训练（GPT）的3.5版和4版、Vicuna FastChat-T5、路径语言模型（PaLM）2版以及Falcon-7B-Instruct。基于一个简单的指令提示，这些模型对源自共情对话（ED）数据集的语句进行回应。使用三个与共情相关的指标，我们将它们的反应与在ED数据集上微调的传统反应生成对话系统以及人类生成的回应进行了比较。值得注意的是，我们发现LLMs的反应在大多数场景中明显更具共情性。我们将我们的发现置于创建共情对话系统的快速进展背景下进行讨论。
英文摘要：
Large Language Models (LLMs) have demonstrated remarkable performance across various information-seeking and reasoning tasks. These computational systems drive state-of-the-art dialogue systems, such as ChatGPT and Bard. They also carry substantial promise in meeting the growing demands of mental health care, albeit relatively unexplored. As such, this study sought to examine LLMs' capability to generate empathetic responses in conversations that emulate those in a mental health counselling setting. We selected five LLMs: version 3.5 and version 4 of the Generative Pre-training (GPT), Vicuna FastChat-T5, Pathways Language Model (PaLM) version 2, and Falcon-7B-Instruct. Based on a simple instructional prompt, these models responded to utterances derived from the EmpatheticDialogues (ED) dataset. Using three empathy-related metrics, we compared their responses to those from traditional response generation dialogue systems, which were fine-tuned on the ED dataset, along with human-generated responses. Notably, we discovered that responses from the LLMs were remarkably more empathetic in most scenarios. We position our findings in light of catapulting advancements in creating empathetic conversational systems.
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。
