用大模型生成共情对话，提升心理支持效果。
Emotional Support with LLM-based Empathetic Dialogue Generation
- 结合提示工程与微调，增强大模型共情对话能力。
- 最佳模型在竞赛中排名第二，表现优异。
- 适合心理健康、人机交互领域研究者参考。
情感支持对话（ESC）旨在通过对话提供共情且有效的心理援助，以应对日益增长的心理健康支持需求。本文针对NLPCC 2025任务8的ESC评测，提出基于大规模语言模型的解决方案，结合提示工程与微调技术。我们探索了参数高效的低秩适配（LoRA）与全参数微调两种策略，以提升模型生成支持性且情境恰当回应的能力。最佳模型在竞赛中排名第二，验证了大模型与有效适配方法结合在ESC任务中的潜力。未来工作将聚焦于增强情感理解与响应个性化，构建更实用可靠的智能情感支持系统。
原文摘要 · Abstract (English)
Emotional Support Conversation (ESC) aims to provide empathetic and effective emotional assistance through dialogue, addressing the growing demand for mental health support. This paper presents our solution for the NLPCC 2025 Task 8 ESC evaluation, where we leverage large-scale language models enhanced by prompt engineering and finetuning techniques. We explore both parameter-efficient Low-Rank Adaptation and full-parameter fine-tuning strategies to improve the model's ability to generate supportive and contextually appropriate responses. Our best model ranked second in the competition, highlighting the potential of combining LLMs with effective adaptation methods for ESC tasks. Future work will focus on further enhancing emotional understanding and response personalization to build more practical and reliable emotional support systems.
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