RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916292401823744 |
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| author | Wang, Zekun Moore Peng, Zhongyuan Que, Haoran Liu, Jiaheng Zhou, Wangchunshu Wu, Yuhan Guo, Hongcheng Gan, Ruitong Ni, Zehao Yang, Jian Zhang, Man Zhang, Zhaoxiang Ouyang, Wanli Xu, Ke Huang, Stephen W. Fu, Jie Peng, Junran |
| author_facet | Wang, Zekun Moore Peng, Zhongyuan Que, Haoran Liu, Jiaheng Zhou, Wangchunshu Wu, Yuhan Guo, Hongcheng Gan, Ruitong Ni, Zehao Yang, Jian Zhang, Man Zhang, Zhaoxiang Ouyang, Wanli Xu, Ke Huang, Stephen W. Fu, Jie Peng, Junran |
| contents | The advent of Large Language Models (LLMs) has paved the way for complex tasks such as role-playing, which enhances user interactions by enabling models to imitate various characters. However, the closed-source nature of state-of-the-art LLMs and their general-purpose training limit role-playing optimization. In this paper, we introduce RoleLLM, a framework to benchmark, elicit, and enhance role-playing abilities in LLMs. RoleLLM comprises four stages: (1) Role Profile Construction for 100 roles; (2) Context-Based Instruction Generation (Context-Instruct) for role-specific knowledge extraction; (3) Role Prompting using GPT (RoleGPT) for speaking style imitation; and (4) Role-Conditioned Instruction Tuning (RoCIT) for fine-tuning open-source models along with role customization. By Context-Instruct and RoleGPT, we create RoleBench, the first systematic and fine-grained character-level benchmark dataset for role-playing with 168,093 samples. Moreover, RoCIT on RoleBench yields RoleLLaMA (English) and RoleGLM (Chinese), significantly enhancing role-playing abilities and even achieving comparable results with RoleGPT (using GPT-4). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_00746 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models Wang, Zekun Moore Peng, Zhongyuan Que, Haoran Liu, Jiaheng Zhou, Wangchunshu Wu, Yuhan Guo, Hongcheng Gan, Ruitong Ni, Zehao Yang, Jian Zhang, Man Zhang, Zhaoxiang Ouyang, Wanli Xu, Ke Huang, Stephen W. Fu, Jie Peng, Junran Computation and Language Artificial Intelligence The advent of Large Language Models (LLMs) has paved the way for complex tasks such as role-playing, which enhances user interactions by enabling models to imitate various characters. However, the closed-source nature of state-of-the-art LLMs and their general-purpose training limit role-playing optimization. In this paper, we introduce RoleLLM, a framework to benchmark, elicit, and enhance role-playing abilities in LLMs. RoleLLM comprises four stages: (1) Role Profile Construction for 100 roles; (2) Context-Based Instruction Generation (Context-Instruct) for role-specific knowledge extraction; (3) Role Prompting using GPT (RoleGPT) for speaking style imitation; and (4) Role-Conditioned Instruction Tuning (RoCIT) for fine-tuning open-source models along with role customization. By Context-Instruct and RoleGPT, we create RoleBench, the first systematic and fine-grained character-level benchmark dataset for role-playing with 168,093 samples. Moreover, RoCIT on RoleBench yields RoleLLaMA (English) and RoleGLM (Chinese), significantly enhancing role-playing abilities and even achieving comparable results with RoleGPT (using GPT-4). |
| title | RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2310.00746 |