# Role-playing Prompt Framework: Generation and Evaluation

arXiv: https://arxiv.org/abs/2406.00627 · HTML: https://mlsanity.com/paper/2406.00627/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2024.

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

- [PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality](https://mlsanity.com/paper/2606.20897/) (2026)

## Abstract (arXiv)

Large language models (LLMs) exhibit impressive proficiency in natural language generation, understanding user instructions, and emulating human-like language use, which has led to significant interest in their application to role-playing scenarios. However, the manual collection of role-specific script data and the evaluation of model performance are resource-intensive processes. This paper introduces a prompt-based framework designed to leverage GPT's capabilities for the generation of role-playing dialogue datasets and the evaluation of role-playing performance. To validate the effectiveness of the GPT-based generation and evaluation, we further incorporate the recall-oriented Rouge-L metric, providing an additional quantitative measure of performance.