用Reddit焦虑帖训练大模型，发现效果提升但毒性增加。
From Reddit to Generative AI: Evaluating Large Language Models for Anxiety Support Fine-tuned on Social Media Data
- 用r/Anxiety社区帖子微调GPT和Llama模型
- 微调后语言质量提升，但毒性与偏见加重
- 适合关注心理健康助手安全性的研究者
日益增长的心理健康支持需求，叠加人力短缺与现实障碍，促使人们探索利用大语言模型（LLMs）提供可扩展、实时的辅助。然而，其在焦虑支持等敏感领域的应用仍缺乏充分研究。本研究系统评估了基于r/Anxiety子版块真实用户生成内容进行提示与微调的GPT与Llama模型在焦虑支持中的潜力。采用融合三类标准的混合方法评估框架：(i) 语言质量，(ii) 安全性与可信度，(iii) 支持性。结果表明，使用自然语言焦虑数据微调后，语言质量提升，但毒性与偏见增加，情感响应能力下降。尽管模型整体共情有限，GPT被评定为更具支持性。研究警示：未经缓解策略地对原始社交媒体内容进行微调存在风险。
原文摘要 · Abstract (English)
The growing demand for accessible mental health support, compounded by workforce shortages and logistical barriers, has led to increased interest in utilizing Large Language Models (LLMs) for scalable and real-time assistance. However, their use in sensitive domains such as anxiety support remains underexamined. This study presents a systematic evaluation of LLMs (GPT and Llama) for their potential utility in anxiety support by using real user-generated posts from the r/Anxiety subreddit for both prompting and fine-tuning. Our approach utilizes a mixed-method evaluation framework incorporating three main categories of criteria: (i) linguistic quality, (ii) safety and trustworthiness, and (iii) supportiveness. Results show that fine-tuning LLMs with naturalistic anxiety-related data enhanced linguistic quality but increased toxicity and bias, and diminished emotional responsiveness. While LLMs exhibited limited empathy, GPT was evaluated as more supportive overall. Our findings highlight the risks of fine-tuning LLMs on unprocessed social media content without mitigation strategies.
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