Hugging Face Trending Papers
Empath: Tracing Multi-Level Emotion Dynamics in Crisis Counseling Dialogues
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
2d ago
Empath: Tracing Multi-Level Emotion Dynamics in Crisis Counseling Dialogues
arXiv:2609.29056v1 Announce Type: cross Abstract: Emotion dynamics are critical for understanding crisis-support conversations, yet most computational work treats emotion as static utterance-level la...
arXiv AI
Jun 10
Expert-Level Crisis Detection in Mental Health Conversations
arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
arXiv Computation and Language
Sep 1
EmoTrace: An Emotion Trajectory-Centered Framework for Psychological Support Dialogue Generation
arXiv:2607.23648v2 Announce Type: replace Abstract: Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The constru...
arXiv AI
Aug 11
EmoPatient: An Emotion-Directed Patient Simulator for Realistic Palliative Care Communication Training
arXiv:2608. 07495v1 Announce Type: cross Abstract: Effective communication during palliative care discussions is a critical clinical skill, yet training clinicians to manage complex patient emotions remains challenging.
arXiv AI
Sep 7
Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors
The paper introduces a method to predict whether volunteer mental‑health crisis counselors will improve their conversational skills early in their careers. It focuses on identifying moments counselors initially struggle with, tracking how they adapt to similar moments in later conversations, and using these early adaptations to forecast long‑term improvement. The approach outperforms baseline models that rely solely on conversation transcripts.
arXiv AI
Sep 11
RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems
The paper introduces RESCUE-BENCH, a benchmark for relation-aware multi‑party emotional support conversation systems. It is built from real couple and family interview data, comprising 191 samples, 7,079 annotated turns, and 1,064.8 minutes of video, and defines six tasks that assess relational understanding and relation‑sensitive support. Experiments with ten large language models show that while they handle local emotional cues reasonably well, they struggle with tasks that require modeling interpersonal relations, such as predicting relation patterns, viewpoints, and support strategies.