StageWell: A Process-Aligned Chinese Corpus for Positive-Psychology Support Dialogue
Computation and LanguageArtificial Intelligence
Summary
The authors created StageWell, a new Chinese dataset that organizes positive psychology support conversations into six clear stages to better help people dealing with emotional distress. They also developed HQS, a detailed method for building and evaluating this data, focusing on improving not just single replies but the whole support process. By training large language models using these stage-based corrections, the authors showed better dialogue quality, more coherent conversation flow, and safer responses. This work suggests that treating supportive chats as multi-step interactions is more effective than looking at each reply alone.
Authors
Yuxiong Wang, Ziwei Lin, Bo Wang, Yu Zhang, Shiguang Ni
Abstract
Positive psychology dialogue aims to support emotional distress and positive resource building, requiring models to produce not only empathetic replies but also coherent progression through a multi-turn support process. Existing resources often reduce supervision to turn-level strategies or holistic preference labels, leaving process position, support function, and local repair targets implicit. We introduce StageWell, a process-aligned Chinese corpus for positive psychology dialogue, together with HQS, a structured protocol for data construction and evaluation. StageWell organizes support into a six-stage support process and uses a multi-agent whole-dialogue rewriting workflow to construct 12,445 SFT instances, 1,849 DPO preference pairs, and a GroundTruth subset of 120 expert-revised dialogues and 977 QA pairs. Guided by HQS, DPO pairs are built as process-localized repairs: flawed model outputs are used as rejected responses, and targeted rewrites under the same context and stage constraint are used as chosen responses. Across four 9B-14B open-source LLMs, this supervision yields robust gains in process control, response quality, and safety. Averaged across models, BERTScore improves by 0.037, Q-Overall increases by 1.32 points, S-exact increases by 0.236, and the H-critical rate decreases by 0.167. These results highlight the value of modeling supportive dialogue as a structured multi-turn support process rather than as single-turn response generation.