There is a need for empathetic and coherent responses in automated chatbot-facilitated psychotherapy sessions. This study addresses the challenge of enhancing the emotional and contextual understanding of large language models (LLMs) in psychiatric applications. We introduce a novel framework that integrates multiple emotion lexicons, including NRC Emotion Lexicon, VADER, WordNet, and SentiWordNet, with state-of-the-art LLMs such as LLAMA 2, Flan-T5, ChatGPT 3.0, and ChatGPT 4.0. The primary dataset comprises over 2,000 therapy session transcripts from the Counseling and Psychotherapy database, covering discussions on anxiety, depression, trauma, and addiction. We segment the transcripts into smaller chunks, enhancing them with lexical features and computing embeddings using BERT, GPT-3, and RoBERTa to capture semantic and emotional nuances. These embeddings are stored in a FAISS vector database, enabling efficient similarity search and clustering based on cosine similarity. Upon user query, the most relevant segments are retrieved and provided as context to the LLMs, significantly improving the models' ability to generate empathetic and contextually appropriate responses. Experimental evaluations demonstrate that in-corporating emotion lexicons enhances empathy, coherence, informativeness, and fluency scores. Our findings highlight the critical role of emotional embeddings in improving LLM performance for psychotherapy.

本研究解决了自动聊天机器人在心理治疗中提供富有同理心且连贯的响应的挑战。我们提出了一种新颖框架，将多种情感词典与最新的大语言模型相结合，通过增强治疗会话的情感和语境理解，实验结果表明，情感嵌入显著提升了生成响应的同理心、连贯性、信息性和流利程度。

基于情感增强嵌入与大语言模型的情感意识响应生成