NLP模型是否擅长追踪思想：叙事理解综述
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
AI总结：
本文综述了叙事理解任务，探讨了其关键特征、定义、分类及评估方法，并提出通过扩展模块化LLMs能力来提升叙事理解的新视角。
摘要：
叙事理解涉及捕捉作者的认知过程，提供关于其知识、意图、信念和欲望的洞察。尽管大语言模型（LLMs）在生成语法上一致的文本方面表现出色，但它们理解作者思想的能力仍存疑。这一局限阻碍了叙事理解的实用应用。在本文中，我们对叙事理解任务进行了全面调查，深入探讨了其关键特征、定义、分类、相关数据集、训练目标、评估指标和限制。此外，我们探索了扩展模块化LLMs能力以解决新叙事理解任务的潜力。通过将叙事理解框架为检索作者想象力线索以概述叙事结构，我们的研究引入了增强叙事理解的新视角。
英文摘要：
Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author's imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension.
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。
