Role-playing Prompt Framework: Generation and Evaluation
This work addresses efficiency challenges for researchers and developers using LLMs in role-playing applications, but it is incremental as it builds on existing GPT capabilities.
The paper tackles the resource-intensive processes of manually collecting role-specific script data and evaluating model performance in role-playing scenarios by introducing a prompt-based framework that leverages GPT for generating dialogue datasets and evaluating performance, with validation using the Rouge-L metric.
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.