对比大模型与人类创作故事中角色的多样性差异。
CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories
- 从叙事学角度分析角色八维特征，超越基础属性。
- 发现大模型角色在整体性与风格化上存在系统性不足。
- 适合关注生成内容质量与创意多样性的研究者参考。
随着大语言模型生成文本的广泛应用，特别是在虚构领域，我们探讨了其生成故事与人类创作故事在角色塑造上的差异。本研究聚焦于角色，借鉴叙事学定义，分析角色的八个复杂维度，如风格化和完整性。这些维度不仅考察角色的基本特征，更关注其在故事中的呈现方式。通过自动推断大模型与人类写作故事中的角色类别，并进行对比分析，我们围绕两大核心问题展开：(1) 大模型与人类故事是否具有相似的角色？(2) 大模型能否生成具有多样化角色的故事？研究涵盖主流大模型生成故事及近期发表的人类创作故事，揭示出若干有趣的相似点、差异点与关键发现。
原文摘要 · Abstract (English)
As LLM-generated text is increasingly used, especially in fictional domains, we explore how much LLM-generated stories differ from human-written stories. In this work, we focus on characters. We borrow definitions from narratology to analyze eight intricate dimensions of character, such as stylization and wholeness. These dimensions consider more than just basic characteristics. They assess how characters are portrayed within their stories. After automatically inferring categories of characters within both LLM and human-written stories, we compare and contrast these two sets of stories. We consider the following overarching questions: (1) Do LLMs and human-written stories have similar characters? and (2) Do LLMs generate stories with a variety of characters? Our analysis includes research questions that focus on stories generated by popular LLMs and recently published human-written stories. We describe a number of interesting similarities, differences and key takeaways.
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