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Role-playing Prompt Framework: Generation and Evaluation (2406.00627v4)

Published 2 Jun 2024 in cs.CL

Abstract: 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.

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Authors (2)
  1. Xun Liu (39 papers)
  2. Zhengwei Ni (3 papers)