Show, Don't Tell: 使用LLM揭示隐式角色塑造
Show, Don't Tell: Uncovering Implicit Character Portrayal using LLMs
AI总结：
本研究提出LIIPA框架，利用大型语言模型揭示小说中隐式角色塑造，生成更丰富的数据集，并在公平性和准确性上优于现有方法。
摘要：
用于分析小说中角色塑造的工具对于作家和文学学者在开发和解读引人入胜的故事方面具有重要价值。现有的工具，例如用于分析虚构角色的可视化工具，主要依赖于角色属性的显式文本指示。然而，角色塑造通常是隐式的，通过行为和举止而非明确陈述来揭示。我们通过利用大型语言模型（LLMs）来揭示隐式角色塑造，从而弥补这一差距。我们首先为这一任务生成一个数据集，该数据集在跨主题相似性、词汇多样性和叙事长度方面均优于现有的叙事文本语料库，如TinyStories和WritingPrompts。然后，我们引入LIIPA（用于推断角色分析中隐式塑造的LLMs），这是一个提示LLMs以揭示角色塑造的框架。LIIPA可配置使用各种类型的中间计算（角色属性词列表、思维链）来推断源文本中虚构角色是如何被塑造的。我们发现LIIPA优于现有方法，并且由于其能够利用完整的叙事上下文，对角色数量（所描绘的独特人物数量）的增加更具鲁棒性。最后，我们研究了角色塑造估计对角色人口统计特征的敏感性，在我们的LIIPA框架中识别出方法间的公平性-准确性权衡——这一现象在算法公平性文献中很常见。尽管存在这种权衡，所有LIIPA变体在公平性和准确性方面均始终优于非LLM基线。我们的工作展示了使用LLMs分析复杂角色以及更好地理解隐式塑造偏见如何在叙事文本中显现的潜在益处。
英文摘要：
Tools for analyzing character portrayal in fiction are valuable for writers and literary scholars in developing and interpreting compelling stories. Existing tools, such as visualization tools for analyzing fictional characters, primarily rely on explicit textual indicators of character attributes. However, portrayal is often implicit, revealed through actions and behaviors rather than explicit statements. We address this gap by leveraging large language models (LLMs) to uncover implicit character portrayals. We start by generating a dataset for this task with greater cross-topic similarity, lexical diversity, and narrative lengths than existing narrative text corpora such as TinyStories and WritingPrompts. We then introduce LIIPA (LLMs for Inferring Implicit Portrayal for Character Analysis), a framework for prompting LLMs to uncover character portrayals. LIIPA can be configured to use various types of intermediate computation (character attribute word lists, chain-of-thought) to infer how fictional characters are portrayed in the source text. We find that LIIPA outperforms existing approaches, and is more robust to increasing character counts (number of unique persons depicted) due to its ability to utilize full narrative context. Lastly, we investigate the sensitivity of portrayal estimates to character demographics, identifying a fairness-accuracy tradeoff among methods in our LIIPA framework -- a phenomenon familiar within the algorithmic fairness literature. Despite this tradeoff, all LIIPA variants consistently outperform non-LLM baselines in both fairness and accuracy. Our work demonstrates the potential benefits of using LLMs to analyze complex characters and to better understand how implicit portrayal biases may manifest in narrative texts.
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