---
title: "Roleplay-doh — paper by Louie et al. (2024) — summary and ideas"
description: "Practice partners for novice counselors are hard to build with LLMs. 25 references, cited by 31 papers here."
authors: ["Ryan Louie", "Ananjan Nandi", "William Fang", "Cheng Chang", "Emma Brunskill", "Diyi Yang"]
year: 2024
venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2024.emnlp-main.591/"
doi: "10.18653/v1/2024.emnlp-main.591"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/louie-2024-roleplay-doh-enabling-domain-experts/"
---
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# Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles

**Authors:** Ryan Louie, Ananjan Nandi, William Fang, Cheng Chang, [Emma Brunskill](https://md.research.elamur.ai/free/en/authors/emma-brunskill/) and [Diyi Yang](https://md.research.elamur.ai/free/en/authors/diyi-yang/) · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.emnlp-main.591/) · [PDF](https://aclanthology.org/2024.emnlp-main.591.pdf) · [DOI 10.18653/v1/2024.emnlp-main.591](https://doi.org/10.18653/v1/2024.emnlp-main.591) · [OpenAlex](https://openalex.org/W4404782385)

**Citations:** 59 citations (OpenAlex, 2026-10-14) · [31](https://md.research.elamur.ai/free/en/papers/louie-2024-roleplay-doh-enabling-domain-experts/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Practice partners for novice counselors are hard to build with LLMs. Real therapy transcripts are private, so fine-tuning is mostly impossible. Simple prompting also gives patients that sound unrealistic, missing traits such as casual speech and pushback. Roleplay-doh is an interactive tool that lets experienced counselors shape an AI patient without writing prompts. While chatting with the simulated patient, the expert leaves comments on individual replies. An LLM turns these comments into general natural-language rules called principles, and the patient's next replies follow them along with a persona description.

Pilot tests showed that GPT-4 often ignored or misused these rules. To fix this, the authors added a principle-adherence pipeline with three steps:
- split complex or situational principles into simple yes/no checks,
- test whether each check applies to the current turn,
- self-refine the reply when needed.

In a within-subjects study with 25 counseling experts, patients built with Roleplay-doh were judged more authentic and closer to real cases, both by their creators and by outside counselors. The method has nothing specific to mental health, so it could likely transfer to other expert-guided social simulations.

## Key points
- Experts give three kinds of feedback: "kudos" (keep this behavior), "critique" (avoid this), and "rewrite" (a better reply). GPT-3.5 converts kudos and critiques into principles. GPT-4 infers a principle by comparing an original reply with its rewrite.
- With plain GPT-4 prompting, about 20% of replies broke principles. The main causes were multipart principles and rules that only apply in certain situations.
- The adherence pipeline raised response quality and principle following by roughly 30% on the downstream task.
- Third-party rankings compared the full pipeline with a no-self-refine baseline. For principle following it won 35% of the time and lost 5%. For dialogue consistency it won 35% and lost 10%, the best result among all ablations.
- Code and data, including the expert-written principles, are released at roleplay-doh.github.io. Known limitations: the tool does not check for conflicting principles, it is text-only, and only one simulated case involved serious mental illness.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Ryan Louie, Ananjan Nandi, William Fang, Cheng Chang, Emma Brunskill and Diyi Yang. 2024. Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{louie2024roleplay,
  title = {{Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles}},
  author = {Ryan Louie, Ananjan Nandi, William Fang, Cheng Chang, Emma Brunskill and Diyi Yang},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.591},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.591}
}
```

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*Updated 30 October 2026*