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arXiv cs.AI· Jiayi Li, Bingsheng He, Zhaomin Wu·· 5 小时前AI 评分37

LongCounsel-8:跨多次心理咨询对话追踪抑郁变化的基准套件

LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues

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LongCounsel-8 是一个用于跨咨询会话追踪抑郁变化的基准,通过系统性流程从真实案例构建 7,749 条五会话轨迹,每个会话按 PHQ-8 预设分数生成。评测采用趋势导向协议,涵盖会话间变化、筛查阈值穿越、轨迹类型和症状级变化。实验显示五个现有方法均不胜任:变化估计全部不如“无变化”基线,最多仅 40% 的阈值穿越被检出,且加入早期会话虽降低变化误差却漏检更多好转。

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Abstract:Large language models (LLMs) are increasingly used to assess depression in counseling conversations, where psychology evidence suggests that tracking change across sessions matters more than any single score. However, existing dialogue benchmarks score each session on its own, and an accurate score for every session does not guarantee that a method follows the changes between sessions. We introduce LongCounsel-8, a benchmark for tracking depression across counseling sessions. Its systematic pipeline takes client profiles from real counseling cases, depression scores from real longitudinal data, and turn length and form from real counseling dialogues, yielding 7,749 five-session trajectories from three LLMs. Every session is generated to express a preset score on the PHQ-8, a standard eight-symptom depression questionnaire. Validation shows that the labels are self-consistent and the dialogues close to real counseling. Following clinical research on treatment monitoring, we propose a trend-based evaluation protocol reflecting real-world tracking needs: changes between sessions, crossings of the screening cutoff, trajectory types, and symptom-level changes. Extensive experiments show all five existing methods tested fall short in tracking trends: none estimates changes better than a no-change baseline, at most 40% of cutoff crossings are detected, and adding earlier sessions lowers change error but causes more missed improvements.
Comments: 25 pages. Dataset: this https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.03507 [cs.LG]
  (or arXiv:2609.03507v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.03507

arXiv-issued DOI via DataCite

Submission history

From: Jiayi Li [view email]
[v1] Thu, 3 Sep 2026 08:07:41 UTC (86 KB)
[v2] Wed, 4 Nov 2026 14:45:21 UTC (62 KB)

来源:arXiv cs.AI · arxiv.org