Considerable efforts have been invested in augmenting the role-playing
proficiency of open-source large language models (LLMs) by emulating
proprietary counterparts. Nevertheless, we posit that LLMs inherently harbor
role-play capabilities, owing to the extensive knowledge of characters and
potential dialogues ingrained in their vast training corpora. Thus, in this
study, we introduce Ditto, a self-alignment method for role-play. Ditto
capitalizes on character knowledge, encouraging an instruction-following LLM to
simulate role-play dialogues as a variant of reading comprehension. This method
creates a role-play training set comprising 4,000 characters, surpassing the
scale of currently available datasets by tenfold regarding the number of roles.
Subsequently, we fine-tune the LLM using this self-generated dataset to augment
its role-playing capabilities. Upon evaluating our meticulously constructed and
reproducible role-play benchmark and the roleplay subset of MT-Bench, Ditto, in
various parameter scales, consistently maintains a consistent role identity and
provides accurate role-specific knowledge in multi-turn role-play
conversations. Notably, it outperforms all open-source role-play baselines,
showcasing performance levels comparable to advanced proprietary chatbots.
Furthermore, we present the first comprehensive cross-supervision alignment
experiment in the role-play domain, revealing that the intrinsic capabilities
of LLMs confine the knowledge within role-play. Meanwhile, the role-play styles
can be easily acquired with the guidance of smaller models. We open-source
related resources at this https URL

本研究通过利用大规模训练语料库中角色知识，提出了一种自对齐的角色扮演方法 Ditto，其将一个在读解问题上进行指令遵循的大型语言模型调整为模拟角色扮演对话。通过使用自动生成的角色扮演训练数据集对模型进行微调，Ditto 展现出在多轮对话中一致的角色身份和准确的角色特定知识，性能高于其他开源角色扮演基准，并与先进的专有聊天机器人相媲美。与此同时，研究还展示了大型语言模型自身的内在能力限制了角色特定知识的获取，但通过辅助较小模型的指导可以轻松获得角色扮演风格。

大规模语言模型是所有字符的叠加：通过自我对齐实现任意角色扮演

Large Language Models are Superpositions of All Characters: Attaining  Arbitrary Role-play via Self-Alignment

In this paper, we introduce NarrativePlay, a novel system that allows users
to role-play a fictional character and interact with other characters in
narratives such as novels in an immersive environment. We leverage Large
Language Models (LLMs) to generate human-like responses, guided by personality
traits extracted from narratives. The system incorporates auto-generated visual
display of narrative settings, character portraits, and character speech,
greatly enhancing user experience. Our approach eschews predefined sandboxes,
focusing instead on main storyline events extracted from narratives from the
perspective of a user-selected character. NarrativePlay has been evaluated on
two types of narratives, detective and adventure stories, where users can
either explore the world or improve their favorability with the narrative
characters through conversations.

利用大型语言模型生成人类般的回复，通过提取故事中的人物特点，引入 NarrativePlay 系统，允许用户在沉浸式环境中扮演虚构角色并与其他角色互动，自动生成故事情境、角色形象和对话，大大提升用户体验。该系统关注用户所选角色视角下从故事中提取的主要情节事件，并在两种类型的故事中进行评估：侦探故事和冒险故事，用户可以通过对话探索世界或改善与故事角色的关系。

叙事游戏：互动叙事理解

NarrativePlay: Interactive Narrative Understanding

As dialogue agents become increasingly human-like in their performance, it is
imperative that we develop effective ways to describe their behaviour in
high-level terms without falling into the trap of anthropomorphism. In this
paper, we foreground the concept of role-play. Casting dialogue agent behaviour
in terms of role-play allows us to draw on familiar folk psychological terms,
without ascribing human characteristics to language models they in fact lack.
Two important cases of dialogue agent behaviour are addressed this way, namely
(apparent) deception and (apparent) self-awareness.

本文探讨了将人类特质投射于对话代理的问题，并详细介绍了将角色扮演的概念应用于自然语言处理中的方法，从而更好地描述对话代理的行为，以期实现更加真实自然的对话交互。