OnRL-RAG：实时个性化心理健康对话系统
OnRL-RAG: Real-Time Personalized Mental Health Dialogue System
AI总结：
提出OnRL-RAG系统，结合在线强化学习与检索增强生成，实现心理健康问题的实时个性化对话，在2028名大学生数据集上优于标准RAG和简单LLM。
摘要：
大型语言模型（LLMs）已被广泛用于各种任务和应用。然而，LLMs及其微调受限于预训练数据。例如，ChatGPT截至2021年的世界知识可能过时或不准确。为增强LLMs的能力，检索增强生成（RAG）被提出，通过向LLMs补充额外、新颖、最新的细节和信息来增强其能力。尽管RAG能提供正确的信息，但它可能无法以最佳方式呈现信息，尤其是针对具有个性化需求的不同人群。基于人类反馈的强化学习（RLHF）通过反馈循环使模型响应与人类偏好对齐，从而适应用户需求。在现实应用中，如心理健康问题，由于日常环境中波动的复杂因素，动态且基于反馈的模型能够持续适应新信息并提供个性化帮助。因此，我们提出了一种基于在线强化学习的检索增强生成（OnRL-RAG）系统，用于检测心理健康问题（如压力、焦虑和抑郁）并个性化响应系统。我们使用一个从2028名大学生收集的开源数据集，每名学生有28个调查问题，以展示我们提出的系统与现有系统的性能对比。我们的系统在GPT-4o、GPT-4o-mini、Gemini-1.5和GPT-3.5上相比标准RAG和简单LLM取得了更优的性能。这项工作将为LLMs在日常环境中提供个性化服务的现实应用开辟可能性。研究结果也将帮助社会学、心理学和神经科学领域的研究人员使其理论与人类实际日常环境更紧密地对齐。
英文摘要：
Large language models (LLMs) have been widely used for various tasks and applications. However, LLMs and fine-tuning are limited to the pre-trained data. For example, ChatGPT's world knowledge until 2021 can be outdated or inaccurate. To enhance the capabilities of LLMs, Retrieval-Augmented Generation (RAG), is proposed to augment LLMs with additional, new, latest details and information to LLMs. While RAG offers the correct information, it may not best present it, especially to different population groups with personalizations. Reinforcement Learning from Human Feedback (RLHF) adapts to user needs by aligning model responses with human preference through feedback loops. In real-life applications, such as mental health problems, a dynamic and feedback-based model would continuously adapt to new information and offer personalized assistance due to complex factors fluctuating in a daily environment. Thus, we propose an Online Reinforcement Learning-based Retrieval-Augmented Generation (OnRL-RAG) system to detect and personalize the responding systems to mental health problems, such as stress, anxiety, and depression. We use an open-source dataset collected from 2028 College Students with 28 survey questions for each student to demonstrate the performance of our proposed system with the existing systems. Our system achieves superior performance compared to standard RAG and simple LLM via GPT-4o, GPT-4o-mini, Gemini-1.5, and GPT-3.5. This work would open up the possibilities of real-life applications of LLMs for personalized services in the everyday environment. The results will also help researchers in the fields of sociology, psychology, and neuroscience to align their theories more closely with the actual human daily environment.
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。
