在大型语言模型中使用知识图谱与向量存储的集成来缓解幻觉以增强心理健康支持
Mitigating Hallucinations Using Ensemble of Knowledge Graph and Vector Store in Large Language Models to Enhance Mental Health Support
AI总结：
本研究探讨大型语言模型在心理健康领域的幻觉问题，通过分析其产生机制并提出针对性干预策略，构建更稳健的框架以提升模型在心理治疗与咨询中的可靠性与安全性。
摘要：
本研究深入探讨了大型语言模型（LLMs）中幻觉的表现及其对心理健康领域应用的影响。主要目标是识别有效策略以减少幻觉发生，从而提升LLMs在心理治疗、咨询及传播相关信息等心理健康干预中的可靠性与安全性。通过严谨的调查与分析，本研究旨在阐明引发LLMs幻觉的潜在机制，并提出针对性干预措施以减轻其发生。通过解决这一关键问题，本研究致力于构建更稳健的框架，确保LLMs在心理健康情境中有效可靠地辅助治疗过程，并向寻求心理健康支持的个体提供准确信息。
英文摘要：
This research work delves into the manifestation of hallucination within Large Language Models (LLMs) and its consequential impacts on applications within the domain of mental health. The primary objective is to discern effective strategies for curtailing hallucinatory occurrences, thereby bolstering the dependability and security of LLMs in facilitating mental health interventions such as therapy, counseling, and the dissemination of pertinent information. Through rigorous investigation and analysis, this study seeks to elucidate the underlying mechanisms precipitating hallucinations in LLMs and subsequently propose targeted interventions to alleviate their occurrence. By addressing this critical issue, the research endeavors to foster a more robust framework for the utilization of LLMs within mental health contexts, ensuring their efficacy and reliability in aiding therapeutic processes and delivering accurate information to individuals seeking mental health support.
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。
