← Back to all news
arXiv AI

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.

Related stories

arXiv Computation and Language
Sep 18

Steering the Compass: Aligning Dynamic Psychological Counseling Conversations with Cognitive Behavioral Therapy Strategies

The paper introduces StratCBT, a new dataset of 9,688 psychological counseling sessions with 256K utterances, each counselor response aligned to one of eight Cognitive Behavioral Therapy (CBT) strategies. It was created by modeling clients’ negative thoughts and generating high‑quality conversations through self‑chat, using realistic sessions for guidance. Experiments show that strategy‑aligned generation improves professional and effective counseling when evaluated with large language model‑simulated clients.

By Zimu Wang, Yiwen Jiang, Xiangyu Zhao, Yaling Shen, Jiahe Liu, Stephanie Fong, Maxmartwell H Cheng, Guilherme C Oliveira, Anh Nguyen, Robert Desimone, Barnaby Nelson, Dominic Dwyer, Zongyuan Ge
llms
More like this →
arXiv Computation and Language
Sep 1

Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs

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.

By Aishik Mandal, Hiba Arnaout, Clarissa W. Ong, Juliet Bockhorst, Kate Sheehan, Rachael Moldow, Tanmoy Chakraborty, Iryna Gurevych
llmsagentsfine-tuningsafety
More like this →
arXiv AI
6d ago

Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generation

The paper introduces the COmmunity-centered Peer Engaged Support (COPES) dataset and a three‑axis evaluation framework to gauge how well Large Language Models (LLMs) align with community perspectives on mental‑health support queries. Experiments show that fine‑tuning LLMs on COPES improves strategy alignment and emotion‑tone alignment by over 50% for general‑purpose models, yet these gains are uneven across subreddits and coping strategies. The study also finds that post‑training shifts the model’s recommendations toward problem‑focused advice while reducing emotion‑focused responses, indicating persistent disparities in performance across different communities and needs.

By Mohit Chandra, Nabin Kim, Eli Min, Aamogh Sawant, Tanmay Sutar, Munmun De Choudhury
llmsreinforcement-learningfine-tuningbenchmarkssafety
More like this →
Hugging Face Trending Papers
Sep 3

LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues

LongCounsel-8 is a benchmark suite comprising three independently generated datasets with 7,749 five‑session counseling trajectories, designed to evaluate longitudinal depression tracking. The datasets are grounded in real‑world client profiles, depression trajectories, symptom compositions, and counseling patterns, and they address challenges of longitudinal consistency, empirical grounding, and natural expression of controlled depression states. Experiments show that lower single‑session error does not guarantee accurate trend identification, that methods perform worse on worsening trajectories, and that adding more session history can reduce trend prediction accuracy.

benchmarks
More like this →
arXiv AI
Sep 4

LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues

LongCounsel-8 is a new benchmark suite comprising three datasets with 7,749 five‑session counseling dialogues, each grounded in real client profiles, depression trajectories, symptom compositions, and counseling patterns. The benchmark addresses challenges of longitudinal consistency, empirical grounding of symptom progression, and natural expression of controlled depression states without exposing labels. Experiments show that lower single‑session error does not ensure accurate trend detection, methods perform worse on worsening trajectories, and adding more session history can reduce trend prediction accuracy.

By Jiayi Li, Zhaomin Wu, Bingsheng He
benchmarks
More like this →
arXiv AI
Aug 26

When Less Is More: An Empirical Study of Minimal Responses in Counseling Dialogues and the Behavior of LLMs

The paper investigates the role of minimal responses—short, empathic utterances—in psychological counseling, noting that such brief replies are common in human dialogues but underrepresented in large language model (LLM) outputs. Using a two‑stage filtering approach and contextual verification with an LLM, the authors systematically analyze minimal responses across multiple counseling datasets. They find that while strong commercial LLMs can produce minimal replies when prompted, they often fail to judge when these replies are appropriate, and counseling‑specific models trained on synthetic data tend to generate longer, content‑rich responses instead.

By Zhiyang Qi
llms
More like this →