Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Abstract

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.

References in corpus (14)

• Training language models to follow instructions with human feedback
• LLaMA: Open and Efficient Foundation Language Models
• Multitask Prompted Training Enables Zero-Shot Task Generalization
• BARTScore: Evaluating Generated Text as Text Generation
• Large Language Models Are Human-Level Prompt Engineers
• HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
• StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement
• UL2: Unifying Language Learning Paradigms
• GPTScore: Evaluate as You Desire
• Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization
• TellMeWhy: A Dataset for Answering Why-Questions in Narratives
• A Bayesian Perspective on Training Speed and Model Selection
• Detecting Narrative Elements in Informational Text
• Modeling Human Mental States with an Entity-based Narrative Graph