arXiv cs.AI· Jiayi Li, Bingsheng He, Zhaomin Wu·· 11 小时前AI 评分36
LongCounsel-8:多会话心理咨询对话中的长期抑郁追踪基准套件
LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues
AI 导读
研究团队推出 LongCounsel-8 基准,用于跨咨询会话追踪抑郁变化,包含由三个 LLM 生成的 7,749 条五会话轨迹,每段对话按 PHQ-8 预设分数生成。该基准采用基于趋势的评估协议,考察会话间变化、筛查阈值跨越、轨迹类型和症状级变化。实验显示所测五种现有方法均无法优于“无变化”基线,阈值跨越检出率最高仅 40%。
正文 · 原文
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