EvolvTrip：利用时序心智理论图增强文学人物理解
EvolvTrip: Enhancing Literary Character Understanding with Temporal Theory-of-Mind Graphs
AI总结：
针对大语言模型在长篇叙事中心智理论推理能力不足的问题，本研究构建了LitCharToM评估基准，并提出时序知识图谱EvolvTrip，可有效提升不同规模模型的人物理解能力，尤其能缩小小模型与大模型的性能差距。
摘要：
生动的人物塑造是叙事作品成功的关键。对读者而言，要领会人物特质，需要具备在复杂故事情节发展过程中推断其不断变化的信念、欲望和意图的能力，这种认知技能被称为心智理论（Theory-of-Mind, ToM）。在长篇叙事中进行ToM推理，需要读者将历史背景与当前叙事信息相结合，这是人类擅长但大语言模型（Large Language Models, LLMs）往往难以胜任的任务。为系统评估LLMs在长篇叙事中的ToM推理能力，我们构建了LitCharToM基准，该基准包含来自经典文学作品、覆盖四个ToM维度的以人物为中心的问题。此外，我们提出了EvolvTrip，这是一种具备视角感知能力的时序知识图谱，可追踪叙事全程中人物的心理发展过程。我们的实验表明，EvolvTrip能持续提升不同规模LLMs的性能，即便在具有挑战性的长上下文场景中也是如此。EvolvTrip对较小规模的模型价值尤为突出，能部分缩小其与更大规模LLMs的性能差距，且与长篇叙事展现出极佳的兼容性。我们的研究结果凸显了在叙事理解中显式表征人物心理状态时序变化的重要性，为更复杂的人物理解研究奠定了基础。我们的数据集和代码已公开发布于https://github.com/Bernard-Yang/EvolvTrip。
英文摘要：
A compelling portrayal of characters is essential to the success of narrative writing. For readers, appreciating a character's traits requires the ability to infer their evolving beliefs, desires, and intentions over the course of a complex storyline, a cognitive skill known as Theory-of-Mind (ToM). Performing ToM reasoning in prolonged narratives requires readers to integrate historical context with current narrative information, a task at which humans excel but Large Language Models (LLMs) often struggle. To systematically evaluate LLMs' ToM reasoning capability in long narratives, we construct LitCharToM, a benchmark of character-centric questions across four ToM dimensions from classic literature. Further, we introduce EvolvTrip, a perspective-aware temporal knowledge graph that tracks psychological development throughout narratives. Our experiments demonstrate that EvolvTrip consistently enhances performance of LLMs across varying scales, even in challenging extended-context scenarios. EvolvTrip proves to be particularly valuable for smaller models, partially bridging the performance gap with larger LLMs and showing great compatibility with lengthy narratives. Our findings highlight the importance of explicit representation of temporal character mental states in narrative comprehension and offer a foundation for more sophisticated character understanding. Our data and code are publicly available at https://github.com/Bernard-Yang/EvolvTrip.
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。
