A Multi-Task Role-Playing Agent Capable of Imitating Character Linguistic Styles
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916468280524800 |
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| author | Chen, Siyuan Si, Qingyi Yang, Chenxu Liang, Yunzhi Lin, Zheng Liu, Huan Wang, Weiping |
| author_facet | Chen, Siyuan Si, Qingyi Yang, Chenxu Liang, Yunzhi Lin, Zheng Liu, Huan Wang, Weiping |
| contents | The advent of large language models (LLMs) has significantly propelled the advancement of Role-Playing Agents (RPAs). However, current Role-Playing Agents predominantly focus on mimicking a character's fundamental attributes while neglecting the replication of linguistic style, and they are incapable of effectively replicating characters when performing tasks beyond multi-turn dialogues, which results in generated responses that lack authenticity. The reason current RPAs lack this capability is due to the nature of existing character datasets, which lack collections of character quotations and are limited to multi-turn dialogue tasks, constraining the RPA's performance across other task domains and failing to mimic a character's linguistic style. To address this gap, we developed a multi-task role-playing dataset named MRstyle, which encompasses a substantial number of real individuals along with their quotations and covers seven different tasks. On this basis, we develop StyleRPA, a Multi-Task Role-Playing Agent (MRPA) that significantly outperforms recent open-source LLMs and RPAs baselines on 7 tasks including Dialogue, Dictionary, Composition, Story Generation, Product Description, Music Commentary, and Open Question Answering. The code and data will be released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02457 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multi-Task Role-Playing Agent Capable of Imitating Character Linguistic Styles Chen, Siyuan Si, Qingyi Yang, Chenxu Liang, Yunzhi Lin, Zheng Liu, Huan Wang, Weiping Computation and Language Artificial Intelligence The advent of large language models (LLMs) has significantly propelled the advancement of Role-Playing Agents (RPAs). However, current Role-Playing Agents predominantly focus on mimicking a character's fundamental attributes while neglecting the replication of linguistic style, and they are incapable of effectively replicating characters when performing tasks beyond multi-turn dialogues, which results in generated responses that lack authenticity. The reason current RPAs lack this capability is due to the nature of existing character datasets, which lack collections of character quotations and are limited to multi-turn dialogue tasks, constraining the RPA's performance across other task domains and failing to mimic a character's linguistic style. To address this gap, we developed a multi-task role-playing dataset named MRstyle, which encompasses a substantial number of real individuals along with their quotations and covers seven different tasks. On this basis, we develop StyleRPA, a Multi-Task Role-Playing Agent (MRPA) that significantly outperforms recent open-source LLMs and RPAs baselines on 7 tasks including Dialogue, Dictionary, Composition, Story Generation, Product Description, Music Commentary, and Open Question Answering. The code and data will be released. |
| title | A Multi-Task Role-Playing Agent Capable of Imitating Character Linguistic Styles |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2411.02457 |