Virtual Agents for Alcohol Use Counseling: Exploring LLM-Powered Motivational Interviewing
- URL: http://arxiv.org/abs/2407.08095v1
- Date: Wed, 10 Jul 2024 23:50:08 GMT
- Title: Virtual Agents for Alcohol Use Counseling: Exploring LLM-Powered Motivational Interviewing
- Authors: Ian Steenstra, Farnaz Nouraei, Mehdi Arjmand, Timothy W. Bickmore,
- Abstract summary: We develop a virtual counselor capable of conducting motivational interviewing (MI) for alcohol use counseling.
Our approach combines prompt engineering and integration into a user-friendly virtual platform to facilitate realistic, empathetic interactions.
- Score: 7.899257236779216
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce a novel application of large language models (LLMs) in developing a virtual counselor capable of conducting motivational interviewing (MI) for alcohol use counseling. Access to effective counseling remains limited, particularly for substance abuse, and virtual agents offer a promising solution by leveraging LLM capabilities to simulate nuanced communication techniques inherent in MI. Our approach combines prompt engineering and integration into a user-friendly virtual platform to facilitate realistic, empathetic interactions. We evaluate the effectiveness of our virtual agent through a series of studies focusing on replicating MI techniques and human counselor dialog. Initial findings suggest that our LLM-powered virtual agent matches human counselors' empathetic and adaptive conversational skills, presenting a significant step forward in virtual health counseling and providing insights into the design and implementation of LLM-based therapeutic interactions.
Related papers
- Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles [58.82161879559716]
We develop Roleplay-doh, a novel human-LLM collaboration pipeline that elicits qualitative feedback from a domain-expert.
We apply this pipeline to enable senior mental health supporters to create customized AI patients for simulated practice partners.
arXiv Detail & Related papers (2024-07-01T00:43:02Z) - Facilitating Multi-Role and Multi-Behavior Collaboration of Large Language Models for Online Job Seeking and Recruiting [51.54907796704785]
Existing methods rely on modeling the latent semantics of resumes and job descriptions and learning a matching function between them.
Inspired by the powerful role-playing capabilities of Large Language Models (LLMs), we propose to introduce a mock interview process between LLM-played interviewers and candidates.
We propose MockLLM, a novel applicable framework that divides the person-job matching process into two modules: mock interview generation and two-sided evaluation in handshake protocol.
arXiv Detail & Related papers (2024-05-28T12:23:16Z) - VR-GPT: Visual Language Model for Intelligent Virtual Reality Applications [2.5022287664959446]
This study introduces a pioneering approach utilizing Visual Language Models within VR environments to enhance user interaction and task efficiency.
Our system facilitates real-time, intuitive user interactions through natural language processing, without relying on visual text instructions.
arXiv Detail & Related papers (2024-05-19T12:56:00Z) - Evaluating the Efficacy of Interactive Language Therapy Based on LLM for
High-Functioning Autistic Adolescent Psychological Counseling [1.1780706927049207]
This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents.
LLMs present a novel opportunity to augment traditional psychological counseling methods.
arXiv Detail & Related papers (2023-11-12T07:55:39Z) - Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations [70.7884839812069]
Large language models (LLMs) have emerged as powerful and general solutions to many natural language tasks.
However, many of the most important applications of language generation are interactive, where an agent has to talk to a person to reach a desired outcome.
In this work, we explore a new method for adapting LLMs with RL for such goal-directed dialogue.
arXiv Detail & Related papers (2023-11-09T18:45:16Z) - LLM-Based Agent Society Investigation: Collaboration and Confrontation
in Avalon Gameplay [57.202649879872624]
We present a novel framework designed to seamlessly adapt to Avalon gameplay.
The core of our proposed framework is a multi-agent system that enables efficient communication and interaction among agents.
Our results demonstrate the effectiveness of our framework in generating adaptive and intelligent agents.
arXiv Detail & Related papers (2023-10-23T14:35:26Z) - Building Emotional Support Chatbots in the Era of LLMs [64.06811786616471]
We introduce an innovative methodology that synthesizes human insights with the computational prowess of Large Language Models (LLMs)
By utilizing the in-context learning potential of ChatGPT, we generate an ExTensible Emotional Support dialogue dataset, named ExTES.
Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions.
arXiv Detail & Related papers (2023-08-17T10:49:18Z) - SAPIEN: Affective Virtual Agents Powered by Large Language Models [2.423280064224919]
We introduce SAPIEN, a platform for high-fidelity virtual agents driven by large language models.
The platform allows users to customize their virtual agent's personality, background, and conversation premise.
After the virtual meeting, the user can choose to get the conversation analyzed and receive actionable feedback on their communication skills.
arXiv Detail & Related papers (2023-08-06T05:13:16Z) - SPA: Verbal Interactions between Agents and Avatars in Shared Virtual
Environments using Propositional Planning [61.335252950832256]
Sense-Plan-Ask, or SPA, generates plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments.
We find that our algorithm creates a small runtime cost and enables agents to complete their goals more effectively than agents without the ability to leverage natural-language communication.
arXiv Detail & Related papers (2020-02-08T23:15:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.