A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient

• URL: http://arxiv.org/abs/2207.07276v1
• Date: Fri, 15 Jul 2022 03:52:00 GMT
• Title: A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient
• Authors: Benjamin Kane, Catherine Giugno, Lenhart Schubert, Kurtis Haut, Caleb Wohn, Ehsan Hoque
• Abstract summary: We describe a general-purpose schema-guided dialogue management framework used to develop SOPHIE, a virtual standardized cancer patient. Our agent is judged to produce responses that are natural, emotionally appropriate, and consistent with her role as a cancer patient.
• Score: 2.1530718840070784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A schema-guided approach to dialogue management has been shown in recent work to be effective in creating robust customizable virtual agents capable of acting as friendly peers or task assistants. However, successful applications of these methods in open-ended, mixed-initiative domains remain elusive -- particularly within medical domains such as virtual standardized patients, where such complex interactions are commonplace -- and require more extensive and flexible dialogue management capabilities than previous systems provide. In this paper, we describe a general-purpose schema-guided dialogue management framework used to develop SOPHIE, a virtual standardized cancer patient that allows a doctor to conveniently practice for interactions with patients. We conduct a crowdsourced evaluation of conversations between medical students and SOPHIE. Our agent is judged to produce responses that are natural, emotionally appropriate, and consistent with her role as a cancer patient. Furthermore, it significantly outperforms an end-to-end neural model fine-tuned on a human standardized patient corpus, attesting to the advantages of a schema-guided approach.

Related papers

• Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations [74.83732294523402]
  We introduce a novel benchmark that simulates real-world diagnostic scenarios, integrating noise and difficulty levels aligned with USMLE standards. We also explore dialogue-based fine-tuning, which transforms static datasets into conversational formats to better capture iterative reasoning processes. Experiments show that dialogue-tuned models outperform traditional methods, with improvements of $9.64%$ in multi-round reasoning scenarios and $6.18%$ in accuracy in a noisy environment.
  arXiv  Detail & Related papers  (2025-01-29T18:58:48Z)
• Script-Based Dialog Policy Planning for LLM-Powered Conversational Agents: A Basic Architecture for an "AI Therapist" [0.0]
  Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support. We introduce a novel paradigm for dialog policy planning in conversational agents enabling them to act according to an expert-written "script" 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.
  arXiv  Detail & Related papers  (2024-12-13T12:12:47Z)
• Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking [58.25862290294702]
  We present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow. We also propose MedChain-Agent, an AI system that integrates a feedback mechanism and a MCase-RAG module to learn from previous cases and adapt its responses.
  arXiv  Detail & Related papers  (2024-12-02T15:25:02Z)
• Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
  We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation. Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
  arXiv  Detail & Related papers  (2024-11-16T18:19:53Z)
• A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making [38.2229221645303]
  Large Language Models (LLMs) promise to streamline this process by synthesizing vast medical knowledge and multi-modal health data. Our MDAgents address this need by dynamically assigning collaboration structures to LLMs based on task complexity. This framework improves diagnostic accuracy and supports adaptive responses in complex, real-world medical scenarios.
  arXiv  Detail & Related papers  (2024-10-31T22:58:08Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• 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)
• Benchmarking Large Language Models on Communicative Medical Coaching: a Novel System and Dataset [26.504409173684653]
  We introduce "ChatCoach", a human-AI cooperative framework designed to assist medical learners in practicing their communication skills during patient consultations. ChatCoachdifferentiates itself from conventional dialogue systems by offering a simulated environment where medical learners can practice dialogues with a patient agent, while a coach agent provides immediate, structured feedback. We have developed a dataset specifically for evaluating Large Language Models (LLMs) within the ChatCoach framework on communicative medical coaching tasks.
  arXiv  Detail & Related papers  (2024-02-08T10:32:06Z)
• PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning [20.437165038293426]
  The patient-centered medical dialogue systems strive to offer diagnostic interpretation services to users who are less knowledgeable about medical knowledge. It is difficult for the large language models (LLMs) to guarantee the specificity of responses in spite of its promising performance. Inspired by in-context learning, we propose PlugMed, a Plug-and-Play Medical Dialogue System.
  arXiv  Detail & Related papers  (2023-05-19T08:18:24Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation [86.38736781043109]
  We build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG. We propose two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset.
  arXiv  Detail & Related papers  (2020-10-15T03:34:33Z)

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.