Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generation

2026-09-17

AI Summary

研究通过构建COPES数据集和评估框架，利用细调大型语言模型来提高在线心理健康支持的社区一致性，但效果在不同子社区和需求中存在差异。

Abstract

As access to professional mental healthcare remains limited, many individuals turn to online platforms such as Reddit to seek peer support situated within human lived experience. However, a significant portion of such queries go unanswered, presenting an opportunity for using Large Language Models (LLMs) to fill this gap. While LLMs have demonstrated strong performance on clinical benchmarks, their ability to generate lived-experience informed and community-aligned peer support is underexplored. Addressing this gap, we introduce the COmmunity-centered Peer Engaged Support (COPES) dataset and a three-axis evaluation framework to assess LLM alignment with community perspectives to mental health support seeking queries. Evaluating zero-shot and post-trained (SFT and DPO) models, we show that post-training on COPES significantly improves Strategy Alignment (>50% for general-purpose models) and alignment in Emotion & Tone. However, we also observe that such improvements are heterogeneous and alignment improvements vary significantly across subreddits and requested coping strategies. Furthermore, post-training induces distributional shifts, heavily favoring problem-focused recommendations while suppressing emotion-focused strategies. Together, this work shows that while curating community-driven data improves the alignment of LLM responses, model performance remains disparate across distinct sub-communities and specific mental health needs.

Problem

• Mental Health Support
• Large Language Models
• Community Alignment
• Peer Support
• Online Platforms

Innovation

• COPES dataset
• three-axis evaluation framework
• post-training
• strategy alignment
• emotion-focused strategies