PERSONAWEAVER：过程化角色生成中超越传统原型的可控多样性
PERSONAWEAVER: Controllable Diversity Beyond Conventional Archetypes in Procedural Character Generation
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- Boston University（波士顿大学）
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中文总结 AI 辅助
针对LLM过程化角色生成中的行为同质化问题，提出PersonaWeaver，通过分离世界构建与行为规范并利用人工策划的道德与对话反应库，在多个设置中实现更广泛的道德和互动多样性。
中文摘要 AI 辅助
过程化角色生成旨在为游戏、模拟和其他虚拟世界填充多样化的角色。大型语言模型（LLM）为扩展这一任务提供了有前景的基础。然而，基于LLM的过程化角色生成仍处于早期阶段：现有方法要么直接生成角色，要么改编从角色库中检索到的档案。正如我们所展示的，这两种方法都会产生行为同质化的群体：角色绝大多数认同积极的道德规范，并以有帮助的、类似助手的反应来回答问题。为了缓解这种同质化，我们引入了PersonaWeaver，它将世界构建与行为规范分离，并通过设置通用的、多样化的、人工策划的道德立场和对话反应库来建模行为。这种设计使我们能够测试LLM在多大程度上可以超越其默认行为模式，跨越不同设置。在十个现实和奇幻设置以及三个LLM上，PersonaWeaver产生了比先前工作更广泛的道德和互动反应分布。其指导还使人际语言、回复长度和情感多样化。它还产生了更少原型化的世界属性组合。代码可在以下网址获取：https://this https URL。
英文摘要
Procedural character generation aims to populate games, simulations, and other virtual worlds with diverse characters. Large language models (LLMs) offer a promising foundation for scaling this task. However, LLM-based procedural character generation remains at an early stage: existing methods either generate characters directly or adapt profiles retrieved from persona banks. As we show, both approaches produce behaviorally homogeneous populations: characters overwhelmingly agree with positive moral norms and respond to questions with helpful, assistant-like reactions. To mitigate this homogenization, we introduce PersonaWeaver, which disentangles world building from behavioral specification and models behavior through setting general, diverse, manually curated banks of moral positions and conversational reactions. This design allows us to test how far LLM(s) can be pushed beyond their default behavioral patterns across settings. Across ten realistic and fantastical settings and three LLM(s), PersonaWeaver produces broader moral and interactional response distributions than prior work. Its guidance also diversifies interpersonal language, response length, and sentiment. It also produces less archetypal combinations of world attributes. Code is available at https://github.com/mqraitem/PersonaWeaver.
