Prompt Framework for Role-playing: Generation and Evaluation

• URL: http://arxiv.org/abs/2406.00627v2
• Date: Fri, 22 Nov 2024 06:19:35 GMT
• Title: Prompt Framework for Role-playing: Generation and Evaluation
• Authors: Xun Liu, Zhengwei Ni,
• Abstract summary: Large language models (LLMs) exhibit impressive proficiency in natural language generation, understanding user instructions, and emulating human-like language use. This project introduces a prompt-based framework designed to leverage GPT's capabilities for the generation of role-playing dialogue datasets.
• Score: 3.2845546753303867
• License:
• Abstract: 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 project 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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