VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing
Not on the map. Why?
Abstract (arXiv)
Reflective listening is a fundamental skill that counselors must acquire to achieve proficiency in motivational interviewing (MI). It involves responding in a manner that acknowledges and explores the meaning of what the client has expressed in the conversation. In this work, we introduce the task of counseling response rewriting, which transforms non-reflective statements into reflective responses. We introduce VERVE, a template-based rewriting system with paraphrase-augmented training and adaptive template updating. VERVE first creates a template by identifying and filtering out tokens that are not relevant to reflections and constructs a reflective response using the template. Paraphrase-augmented training allows the model to learn less-strict fillings of masked spans, and adaptive template updating helps discover effective templates for rewriting without significantly removing the original content. Using both automatic and human evaluations, we compare our method against text rewriting baselines and show that our framework is effective in turning non-reflective statements into more reflective responses while achieving a good content preservation-reflection style trade-off.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 14 Nov 2023.
Builds on
15 of 42 references.
References · 15
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
- 2018Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfercited by 191
- 2020A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Supportcited by 182
- 2020Deep Learning for Text Style Transfer: A Surveycited by 161
- 2019Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representationcited by 84
- 2020Politeness Transfer: A Tag and Generate Approachcited by 78
- 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interactioncited by 77
- 2021Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approachcited by 75
Built on by
Citing papers per year: 2024: 4; 2025: 2.
6 papers.
- 2024A Computational Framework for Behavioral Assessment of LLM Therapistscited by 61
- 2024Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselorscited by 16
- 2024Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapycited by 7
- 2025A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quitcited by 5
- 2024A Survey of Text Style Transfer: Applications and Ethical Implicationscited by 0
- 2025Mitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarizationcited by 0
- 2025Mitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarization28 Nov 2025
- 2025A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit23 May 2025
- 2024Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy11 Nov 2024
- 2024A Survey of Text Style Transfer: Applications and Ethical Implications23 Jul 2024
- 2024Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors21 Mar 2024
- 2024A Computational Framework for Behavioral Assessment of LLM Therapists1 Jan 2024