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:2607. 08625v1 Announce Type: new Abstract: Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment.
arXiv:2607. 07824v1 Announce Type: cross Abstract: Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive storytelling.
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...
EmoMed is a multimodal medical consultation agent that tailors its responses to users' emotional states—such as anxiety, confusion, or urgency—while preserving clinical accuracy. It processes text and medical images, detects affect indicators, and adjusts tone, structure, and detail accordingly. The system ensures factual reliability through a dual retrieval mechanism that combines web-based fact‑checking with an API‑connected, continuously updated medical knowledge base, and it has been evaluated across seven state‑of‑the‑art language models using comprehensive metrics, showing that emotionally adaptive responses outperform neutral baselines without sacrificing accuracy.
arXiv:2606. 17441v1 Announce Type: cross Abstract: Simulating realistic patient interactions is a key requirement to testing clinical applications of LLMs at scale without time-consuming and expensive user studies.
The paper introduces SIC-Agents, a self‑improving framework designed to enhance simulation for pediatric serious illness communication (SIC) training. It presents two new benchmark suites—PitfallBench and DialogueBench—that assess simulators at both turn‑level and full‑dialogue levels, specifically addressing the unique challenges of multi‑party interactions and parental distress. Experiments demonstrate that SIC‑Agents surpasses static expert prompting, and the authors release the benchmarks for broader research use.
arXiv:2504. 11837v3 Announce Type: replace-cross Abstract: Emotional support conversation (ESC) aims to alleviate people's emotional distress through effective conversations.
arXiv:2608.21925v1 Announce Type: new Abstract: Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. How...
Graph2Counsel is a framework that generates synthetic counseling dialogues by leveraging Client Psychological Graphs (CPGs) to encode the relationships among a client’s thoughts, emotions, and behaviors. The system uses a structured prompting pipeline guided by counselor strategies and explores techniques such as Chain‑of‑Thought and Multi‑Agent Feedback to produce 760 realistic sessions from 76 CPGs. Expert evaluation shows the dataset surpasses previous ones in specificity, counselor competence, authenticity, conversational flow, and safety, and fine‑tuning an open‑source model on it improves performance on several counseling benchmarks.
arXiv:2606. 30491v1 Announce Type: cross Abstract: Background.
arXiv:2602.01995v2 Announce Type: replace Abstract: Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incomp...
The paper introduces a three-tier persona vector for user simulation in evaluating LLM agents, comprising 23 dimensions across demographics, behavioral traits, and emotional states, plus a query-complexity overlay. It demonstrates that these nuanced personas generate diverse, scenario-reactive conversations, leading to significant variations in agent goal achievement and compliance across different contexts. The model’s design allows for reproducible, auditable user behavior patterns without relying on learned covariance matrices.