mlsanityData to 7 Oct 2026

Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning

arXiv 2403.15737 ↗First posted 23 Mar 2024cs.CLcited by 11

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. Addressing such a task requires a system that can infer textithow to motivate a user effectively. We propose DIIT, a framework that is capable of learning and applying conversation strategies in the form of natural language inductive rules from expert demonstrations. Automatic and human evaluation on instruction-following large language models show natural language strategy descriptions discovered by DIIR can improve active listening skills, reduce unsolicited advice, and promote more collaborative and less authoritative responses, outperforming various demonstration utilization methods.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 18 of its 34 references that are in the map. First posted on arXiv 23 Mar 2024.

Builds on

18 of 34 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  5. 2023PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimizationcited by 226
  6. 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 207
  7. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 159
  8. 2023Language Models Meet World Models: Embodied Experiences Enhance Language Modelscited by 123
  9. 2023Hypothesis Search: Inductive Reasoning with Language Modelscited by 119
  10. 2023The Generative AI Paradox: "What It Can Create, It May Not Understand"cited by 97
  11. 2023Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinementcited by 89
  12. 2023Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modellingcited by 88
6 more
  1. 2023CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalizationcited by 88
  2. 2017Unified Pragmatic Models for Generating and Following Instructionscited by 55
  3. 2023kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inferencecited by 47
  4. 2022Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platformcited by 20
  5. 2023Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategycited by 15
  6. 2020Expressive Interviewing: A Conversational System for Coping with COVID-19cited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 6.

11 papers.

  1. 2024The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trendscited by 11
  2. 2025CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Explorationcited by 11
  3. 2025Substance over Style: Evaluating Proactive Conversational Coaching Agentscited by 6
  4. 2025A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quitcited by 5
  5. 2026Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversationcited by 3
  6. 2026METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialoguescited by 2
  7. 2026LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedbackcited by 2
  8. 2025AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewingcited by 1
  9. 2026GenAI for Social Work Field Education: Client Simulation with Real-Time Feedbackcited by 0
  10. 2026StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generationcited by 0
  11. 2026MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counselingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models
  2. 2026User-Aware Active Knowledge Acquisition for Emotional Support Dialogue
  3. 2024Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies
  4. 2024Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health
  5. 2024Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues
  6. 2023Contrastive Learning for Inference in Dialogue
  7. 2023Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach
  8. 2026CALM-IT: Generating Realistic Long-Form Motivational Interviewing Dialogues with Dual-Actor Conversational Dynamics Tracking