大语言模型时代的情感支持聊天机器人构建
Building Emotional Support Chatbots in the Era of LLMs
AI总结：
针对情感支持对话数据有限、缺乏训练范式的问题，研究结合人类经验与大语言模型能力构建ExTES数据集，并微调LLaMA得到专用模型，验证了情感支持能力，推动该领域发展。
摘要：
将情感支持融入各类对话场景具有深远的社会价值，应用场景涵盖社交互动、心理健康咨询和客户服务等领域。然而，该领域的实际应用仍面临诸多未解决的挑战，包括可用数据有限以及缺乏公认的模型训练范式。本研究致力于利用大语言模型（Large Language Models, LLMs）的能力应对这些挑战。我们提出了一种创新方法，将人类洞见与LLMs的计算能力相结合，构建了一个大规模的情感支持对话数据集。该方法首先以精心设计的覆盖多场景的对话集作为生成种子，利用ChatGPT的上下文学习能力，递归生成一个可扩展情感支持对话数据集，命名为ExTES。随后，我们在LLaMA模型上采用先进的微调技术，探究不同训练策略的影响，最终得到一个专为情感支持交互优化的LLM。对所得模型的全面评估表明，其在提供情感支持方面表现出色，标志着情感支持机器人领域迈出了关键一步，为后续研究和落地应用铺平了道路。
英文摘要：
The integration of emotional support into various conversational scenarios presents profound societal benefits, such as social interactions, mental health counseling, and customer service. However, there are unsolved challenges that hinder real-world applications in this field, including limited data availability and the absence of well-accepted model training paradigms. This work endeavors to navigate these challenges by harnessing the capabilities of Large Language Models (LLMs). We introduce an innovative methodology that synthesizes human insights with the computational prowess of LLMs to curate an extensive emotional support dialogue dataset. Our approach is initiated with a meticulously designed set of dialogues spanning diverse scenarios as generative seeds. By utilizing the in-context learning potential of ChatGPT, we recursively generate an ExTensible Emotional Support dialogue dataset, named ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions. An exhaustive assessment of the resultant model showcases its proficiency in offering emotional support, marking a pivotal step in the realm of emotional support bots and paving the way for subsequent research and implementations.
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。
