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:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
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: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.
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.
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.
Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interpersonal relations in multi-party scenarios under...
arXiv:2504. 11837v3 Announce Type: replace-cross Abstract: Emotional support conversation (ESC) aims to alleviate people's emotional distress through effective conversations.
arXiv:2607. 28648v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing.
Crises alter both how people move and how they communicate. During emergencies such as wildfires and pandemics, changes in mobility patterns and online emotional discourse evolve jointly, yet they are typically studied in isolation.
The paper examines how changes made by platforms can disrupt users’ relationships with AI companions. It catalogs 30 disruption events, creates a taxonomy of six types, and identifies three main causes. A risk‑assessment framework with four dimensions is proposed, and a Bayesian time‑series analysis of Reddit data shows that disruptions trigger spikes in anxiety, stress, suicidal expression, and grief, especially when relational continuity and transition support are lacking.
arXiv:2607. 14769v1 Announce Type: cross Abstract: Existing text summarization research has focused much on monologic information (e.