arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2411.02457cs.CLcs.AI
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多任务角色扮演智能体:能够模仿角色语言风格

A Multi-Task Role-Playing Agent Capable of Imitating Character Linguistic Styles

Institute of Information Engineering, Chinese Academy of Sciences(中国科学院信息工程研究所);School of Cyber Security, University of Chinese Academy of Sciences(中国科学院大学网络空间安全学院);Huawei Technologies Co., Ltd.(华为技术有限公司)

Siyuan Chen, Qingyi Si, Chenxu Yang, Yunzhi Liang, Zheng Lin, Huan Liu, Weiping Wang

AI总结:

针对现有角色扮演智能体忽视语言风格且局限于多轮对话的问题,本文构建了含真实人物语录的多任务数据集MRstyle,并开发了StyleRPA智能体,在七项任务上显著超越现有基线。

摘要:

大型语言模型(LLMs)的出现显著推动了角色扮演智能体(RPAs)的发展。然而,当前的角色扮演智能体主要侧重于模仿角色的基本属性,而忽视了语言风格的复现,并且在进行多轮对话之外的任务时无法有效复现角色,导致生成的回复缺乏真实性。当前RPAs缺乏这种能力的原因在于现有角色数据集的特性,这些数据集缺乏角色语录的收集,并且仅限于多轮对话任务,限制了RPA在其他任务领域的表现,也无法模仿角色的语言风格。为解决这一不足,我们开发了一个名为MRstyle的多任务角色扮演数据集,该数据集包含了大量真实人物及其语录,并涵盖了七种不同的任务。在此基础上,我们开发了StyleRPA,一种多任务角色扮演智能体(MRPA),在包括对话、词典、作文、故事生成、产品描述、音乐评论和开放问答在内的7项任务上,显著优于最近的开源LLMs和RPAs基线。代码和数据将公开发布。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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