Authors
Xianbin Wang, Ruolin Wang, Wencheng Yang, Weijun Wang, Wei Pan, Min Jia, Shijie Gao, Xin Zhao
Published in
Journal of medical Internet research. Volume 28. Pages e95603. Oct 29, 2026. Epub Oct 29, 2026.
Abstract
Large language models (LLMs) are increasingly being used for text-based emotional support. However, general-purpose LLMs may not consistently recognize emotional cues, adapt supportive strategies to contexts, or generate responses aligned with psychologically informed communication. The limited availability of large-scale, structured Chinese counseling data further constrains the development of models for context-sensitive emotional support.
We developed and evaluated YuanChat, a family of specialized LLMs for context-sensitive emotional support; assessed whether E-URIAL improved response quality; and compared YuanChat with base LLMs.
We constructed 2 datasets from anonymized real-world counseling sessions: ECD-3 (104,897 counselor response turns) for fine-tuning and Dgold (9413 manually annotated counselor responses). Annotations covered emotional support conversation stages (exploration, comfort, and action), counseling techniques (clarification, support, and challenge), and empathic effects. We developed 2 YuanChat variants using GLM-4-9B-Chat and Huozi 3.5 as backbone models using parameter-efficient fine-tuning with low-rank adaptation (LoRA). We then developed E-URIAL, an inference-time framework combining predicted emotion and support strategy labels with retrieved labeled exemplars. Given the dialogue history through the help seeker's latest utterance, each model generated a counselor-style response. We compared YuanChat with GPT-4, ERNIE 4.0, CharacterGLM, GLM-4-9B-Chat, and Huozi 3.5. The evaluation comprised (1) automatic evaluation on in-domain and external datasets, assessing empathy identification and empathic expression; (2) evaluation of empathy-related situational reasoning using an expert-developed multiple-choice benchmark; (3) evaluation by clinical experts (n=8) of empathic attunement, support strategy appropriateness, perceived helpfulness, and safety and professional boundary adherence; and (4) a complementary evaluation by volunteer master's students in clinical and counseling psychology (n=50) of response fluency, empathy, and perceived helpfulness.
Fine-tuning increased YuanChat-Huozi3.5's empathic expression score from 0.57 to 0.68; empathy identification score increased from 0.54 to 0.59. In expert evaluation, YuanChat-Huozi3.5 received a higher empathic attunement score than the base Huozi3.5 model (1.279 vs 0.327; mean difference 0.952, 95% CI 0.746-1.152). Using professional counselor responses as the reference, the mean differences for YuanChat-Huozi3.5 were 0.148, 0.079, 0.039, and 0.002 in empathic attunement, supportive appropriateness, perceived helpfulness, and safety and professional boundary adherence, respectively. No statistically significant differences were detected between YuanChat-Huozi3.5 and the counselor references (all Holm-adjusted P≥.29). Higher scores were observed when E-URIAL was applied. When E-URIAL was applied with 3 retrieved exemplars, YuanChat-Huozi3.5 achieved an empathy identification score of 0.69 versus 0.59 without E-URIAL, an empathic expression score of 0.79 versus 0.68, and a perceived helpfulness score of 0.74 versus 0.61.
The findings indicate that fine-tuning on real-world counseling data and annotating models with structured emotional support labels improved context-sensitive empathic responding in LLMs. Without updating model parameters, E-URIAL further improved empathy identification and empathic expression across multiple models. Together, these methods provide a scalable, theory-informed approach to enhancing the emotional support capabilities of LLMs.
PMID:
42956309
Bibliographic data and abstract were imported from PubMed on 30 Oct 2026.
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