Script-Based Dialog Policy Planning for LLM-Powered Conversational Agents: A Basic Architecture for an "AI Therapist"

• URL: http://arxiv.org/abs/2412.15242v1
• Date: Fri, 13 Dec 2024 12:12:47 GMT
• Title: Script-Based Dialog Policy Planning for LLM-Powered Conversational Agents: A Basic Architecture for an "AI Therapist"
• Authors: Robert Wasenmüller, Kevin Hilbert, Christoph Benzmüller,
• Abstract summary: Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support.<n>We introduce a novel paradigm for dialog policy planning in conversational agents enabling them to act according to an expert-written "script"<n>We implement two variants of Script-Based Dialog Policy Planning using different prompting techniques and synthesize a total of 100 conversations with LLM-simulated patients.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support, and potentially even deliver full-scale "AI therapy'" in the future. While such agents can already conduct fluent and proactive emotional support conversations, they inherently lack the ability to (a) consistently and reliably act by predefined rules to align their conversation with an overarching therapeutic concept and (b) make their decision paths inspectable for risk management and clinical evaluation -- both essential requirements for an "AI Therapist". In this work, we introduce a novel paradigm for dialog policy planning in conversational agents enabling them to (a) act according to an expert-written "script" that outlines the therapeutic approach and (b) explicitly transition through a finite set of states over the course of the conversation. The script acts as a deterministic component, constraining the LLM's behavior in desirable ways and establishing a basic architecture for an AI Therapist. We implement two variants of Script-Based Dialog Policy Planning using different prompting techniques and synthesize a total of 100 conversations with LLM-simulated patients. The results demonstrate the feasibility of this new technology and provide insights into the efficiency and effectiveness of different implementation variants.

Related papers

• TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments [8.618945530676614]
  This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior. We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for PTSD. We develop a patient simulation approach based on real-life interview transcripts to replace time-consuming and costly manual testing by clinicians.
  arXiv  Detail & Related papers  (2025-04-30T17:58:06Z)
• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users. This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric Diagnosis [10.8749978349074]
  Most conversational AI systems operate reactively, responding to user prompts without guiding the interaction. We introduce ProAI, a goal-oriented, proactive conversational AI framework. We demonstrate that ProAI achieves up to 83.3% accuracy in mental disorder differential diagnosis.
  arXiv  Detail & Related papers  (2025-02-28T03:45:39Z)
• Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy [17.07905574770501]
  Current systems rely on rigid, rule-based designs, heavily dependent on expert-crafted scripts for guiding therapeutic conversations. Recent advances in large language models (LLMs) offer the potential for more flexible interactions, but lack controllability and transparency. We propose Script-Strategy Aligned Generation (SSAG)'', a flexible alignment approach that reduces reliance on fully scripted content.
  arXiv  Detail & Related papers  (2024-11-11T05:14:14Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Planning with Large Language Models for Conversational Agents [51.12859325330882]
  Controllability and proactivity are crucial properties of autonomous conversational agents (CAs) We propose a new framework for planning-based conversational agents powered by large language models (LLMs) Experiment results show that LLMs finetuned on PCA-D can significantly improve the performance and generalize to unseen domains.
  arXiv  Detail & Related papers  (2024-07-04T12:23:02Z)
• Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models [17.236038165057817]
  We leverage a decision transformer architecture for topic recommendation in counseling conversations. The architecture is utilized for offline reinforcement learning. We propose a novel system of utilizing our model's output as synthetic labels for fine-tuning a large language model.
  arXiv  Detail & Related papers  (2024-05-08T13:55:25Z)
• Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts [4.403408362362806]
  We introduce the Chain-of-Interaction prompting method to contextualize large language models for psychiatric decision support by the dyadic interactions. This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding.
  arXiv  Detail & Related papers  (2024-03-20T17:47:49Z)
• 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)
• Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents [121.46051697742608]
  We introduce a new dialogue policy planning paradigm to strategize dialogue problems with a tunable language model plug-in named PPDPP. Specifically, we develop a novel training framework to facilitate supervised fine-tuning over available human-annotated data. PPDPP consistently and substantially outperforms existing approaches on three different proactive dialogue applications.
  arXiv  Detail & Related papers  (2023-11-01T03:20:16Z)
• 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)
• Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration [72.04629217161656]
  This work focuses on three aspects of proactive dialogue systems: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of LLMs, we propose the Proactive Chain-of-Thought prompting scheme.
  arXiv  Detail & Related papers  (2023-05-23T02:49:35Z)

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.