REVIEW 7 cited by
ChatCounselor: A Large Language Models for Mental Health Support
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
ChatCounselor: A Large Language Models for Mental Health Support
read the original abstract
This paper presents ChatCounselor, a large language model (LLM) solution designed to provide mental health support. Unlike generic chatbots, ChatCounselor is distinguished by its foundation in real conversations between consulting clients and professional psychologists, enabling it to possess specialized knowledge and counseling skills in the field of psychology. The training dataset, Psych8k, was constructed from 260 in-depth interviews, each spanning an hour. To assess the quality of counseling responses, the counseling Bench was devised. Leveraging GPT-4 and meticulously crafted prompts based on seven metrics of psychological counseling assessment, the model underwent evaluation using a set of real-world counseling questions. Impressively, ChatCounselor surpasses existing open-source models in the counseling Bench and approaches the performance level of ChatGPT, showcasing the remarkable enhancement in model capability attained through high-quality domain-specific data.
Forward citations
Cited by 7 Pith papers
-
From Pre-trained Models to Large Language Models: A Comprehensive Survey of AI-Driven Psychological Computing
The paper introduces a new taxonomy that groups AI-driven psychological computing tasks by their underlying computational patterns into four categories and reviews over 300 works from the pre-trained model to LLM eras.
-
Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs
Creates a clinical crisis taxonomy and 2,252-example dataset then audits five LLMs, finding variable safety with notable failures on indirect signals and in self-harm categories.
-
EmoTrack: Robust Depression Tracking from Counseling Transcripts across Session Regimes
EmoTrack uses LLM clinical signals plus frozen turn-level embeddings and compact cross-session memory to predict PHQ-8 scores, delivering a 13.5% MAE reduction on single-session DAIC-WOZ and competitive results on the...
-
A Survey of Large Language Models for Perception and Measurement of Human Psychology
A survey proposing a three-pillar framework to evaluate LLMs as tools for measuring latent psychological constructs and reviewing applications in personality and mental health.
-
Exploring Expert Perspectives on Wearable-Triggered LLM Conversational Support for Daily Stress Management
EmBot combines wearable-triggered stress detection with LLM conversational support and was probed via expert interviews to surface design considerations for daily stress management.
-
PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor
PsychAgent combines memory-augmented planning, trajectory-based skill evolution, and rejection fine-tuning to create a self-improving AI psychological counselor that outperforms general LLMs in multi-session evaluations.
-
Moodie: An Early-Stage Design Exploration for Supporting Fear of Missing Out with LLM-based Chatbots
A purpose-built LLM chatbot for FoMO support showed higher engagement than GPT-4o in a preliminary N=21 one-week study while producing similar FoMO reduction.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.