用知识图谱与向量库融合降低大模型幻觉，提升心理健康服务可靠性
Mitigating Hallucinations Using Ensemble of Knowledge Graph and Vector Store in Large Language Models to Enhance Mental Health Support
- 构建知识图谱与向量库的集成系统，动态验证生成内容
- 在心理支持场景中将幻觉率降低40%以上，关键信息准确率超95%
- 适合医疗AI、心理咨询系统开发者参考使用
本研究探讨大型语言模型（LLMs）在心理健康领域应用中的幻觉现象及其影响，旨在提出有效策略以减少幻觉发生，增强LLMs在治疗、咨询及信息传播等场景下的可靠性与安全性。通过深入分析幻觉产生的机制，研究提出了针对性干预方法。该工作致力于建立更稳健的LLM应用框架，确保其在心理支持过程中提供准确、可信的信息，从而提升治疗效果与用户信任度。
原文摘要 · Abstract (English)
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.
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