# Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies

arXiv: https://arxiv.org/abs/2408.06527 · HTML: https://mlsanity.com/paper/2408.06527/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Complexity-Based Prompting for Multi-Step Reasoning](https://mlsanity.com/paper/2210.00720/) (2022)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations](https://mlsanity.com/paper/2305.10172/) (2023)
- [InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning](https://mlsanity.com/paper/2205.12673/) (2022)
- [Understanding Client Reactions in Online Mental Health Counseling](https://mlsanity.com/paper/2306.15334/) (2023)
- [Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations](https://mlsanity.com/paper/2312.13871/) (2023)
- [CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior](https://mlsanity.com/paper/2302.01935/) (2023)
- [Context-dependent Instruction Tuning for Dialogue Response Generation](https://mlsanity.com/paper/2311.07006/) (2023)

## Built on by (most cited first, 10 of 10)

- [Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis](https://mlsanity.com/paper/2508.11398/) (2025)
- [Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy](https://mlsanity.com/paper/2411.06723/) (2024)
- [Affective Flow Language Model for Emotional Support Conversation](https://mlsanity.com/paper/2602.08826/) (2026)
- [Therapy as an NLP Task: Comparing LLMs and Human Peers Behaviors in CBT Sessions](https://mlsanity.com/paper/2409.02244/) (2024)
- [When LLM Therapists Become Salespeople: Evaluating Large Language Models for Ethical Motivational Interviewing](https://mlsanity.com/paper/2503.23566/) (2025)
- [MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction](https://mlsanity.com/paper/2507.06909/) (2025)
- [StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation](https://mlsanity.com/paper/2605.27393/) (2026)
- [Expert-Level Crisis Detection in Mental Health Conversations](https://mlsanity.com/paper/2606.10380/) (2026)
- [MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling](https://mlsanity.com/paper/2606.29265/) (2026)
- [Multi-Objective Aligned Small Language Model Framework for SUD Patient Dialogue Generation](https://mlsanity.com/paper/2610.09209/) (2026)

## Abstract (arXiv)

Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI). However, the inherent lack of transparency in LLM outputs presents significant challenges given the sensitive nature of psychotherapy. Applying MI strategies, a set of MI skills, to generate more controllable therapeutic-adherent conversations with explainability provides a possible solution. In this work, we explore the alignment of LLMs with MI strategies by first prompting the LLMs to predict the appropriate strategies as reasoning and then utilizing these strategies to guide the subsequent dialogue generation. We seek to investigate whether such alignment leads to more controllable and explainable generations. Multiple experiments including automatic and human evaluations are conducted to validate the effectiveness of MI strategies in aligning psychotherapy dialogue generation. Our findings demonstrate the potential of LLMs in producing strategically aligned dialogues and suggest directions for practical applications in psychotherapeutic settings.