# coTherapist: A Behavior-Aligned Small Language Model to Support Mental Healthcare Experts

arXiv: https://arxiv.org/abs/2601.10246 · HTML: https://mlsanity.com/paper/2601.10246/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 71 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 24 of 24)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://mlsanity.com/paper/2310.01352/) (2023)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs](https://mlsanity.com/paper/2305.11792/) (2023)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring](https://mlsanity.com/paper/2310.15461/) (2023)
- [BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data](https://mlsanity.com/paper/2410.16491/) (2024)
- [InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation](https://mlsanity.com/paper/2112.01589/) (2021)
- [Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds](https://mlsanity.com/paper/2403.06609/) (2024)
- [Exploring the Impact of Personality Traits on LLM Bias and Toxicity](https://mlsanity.com/paper/2502.12566/) (2025)
- [Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study](https://mlsanity.com/paper/2402.19052/) (2024)
- [Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling](https://mlsanity.com/paper/2301.12729/) (2023)
- [TutoAI: A Cross-domain Framework for AI-assisted Mixed-media Tutorial Creation on Physical Tasks](https://mlsanity.com/paper/2403.08049/) (2024)
- [Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey](https://mlsanity.com/paper/2511.07448/) (2025)
- [From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers](https://mlsanity.com/paper/2511.03235/) (2025)
- [A Comprehensive Review of Datasets for Clinical Mental Health AI Systems](https://mlsanity.com/paper/2508.09809/) (2025)
- [AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewing](https://mlsanity.com/paper/2505.17380/) (2025)

## Built on by (most cited first, 1 of 1)

- [When Retrieval Helps: Selective Retrieval for Single-Turn Mental-Health QA](https://mlsanity.com/paper/2609.03454/) (2026)

## Abstract (arXiv)

Access to mental healthcare is increasingly strained by workforce shortages and rising demand, motivating the development of intelligent systems that can support mental healthcare experts. We introduce coTherapist, a unified framework utilizing a small language model to emulate core therapeutic competencies through domain-specific fine-tuning, retrieval augmentation, and agentic reasoning. Evaluation on clinical queries demonstrates that coTherapist generates more relevant and clinically grounded responses than contemporary baselines. Using our novel T-BARS rubric and psychometric profiling, we confirm coTherapist exhibits high empathy and therapist-consistent personality traits. Furthermore, human evaluation by domain experts validates that coTherapist delivers accurate, trustworthy, and safe responses. coTherapist was deployed and tested by clinical experts. Collectively, these findings demonstrate that small models can be engineered to exhibit expert-like behavior, offering a scalable pathway for digital mental health tools.