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

arXiv: https://arxiv.org/abs/2310.18783 · HTML: https://mlsanity.com/paper/2310.18783/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 18 of its 188 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [GPTScore: Evaluate as You Desire](https://mlsanity.com/paper/2302.04166/) (2023)
- [ScienceWorld: Is your Agent Smarter than a 5th Grader?](https://mlsanity.com/paper/2203.07540/) (2022)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://mlsanity.com/paper/2210.07197/) (2022)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [WikiHow: A Large Scale Text Summarization Dataset](https://mlsanity.com/paper/1810.09305/) (2018)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://mlsanity.com/paper/1511.02301/) (2015)
- [Dissociating language and thought in large language models](https://mlsanity.com/paper/2301.06627/) (2023)
- [Adapting Pretrained Text-to-Text Models for Long Text Sequences](https://mlsanity.com/paper/2209.10052/) (2022)
- [Embracing data abundance: BookTest Dataset for Reading Comprehension](https://mlsanity.com/paper/1610.00956/) (2016)
- [The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries](https://mlsanity.com/paper/1912.13082/) (2019)
- [AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature](https://mlsanity.com/paper/2302.06579/) (2023)

## Built on by (most cited first, 9 of 9)

- [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://mlsanity.com/paper/2405.17633/) (2024)
- [NarraBench: A Comprehensive Framework for Narrative Benchmarking](https://mlsanity.com/paper/2510.09869/) (2025)
- [PLUGH: A Benchmark for Spatial Understanding and Reasoning in Large Language Models](https://mlsanity.com/paper/2408.04648/) (2024)
- [Retrieving Climate Change Disinformation by Narrative](https://mlsanity.com/paper/2603.22015/) (2026)
- [Computational Analysis of Character Development in Holocaust Testimonies](https://mlsanity.com/paper/2412.17063/) (2024)
- [NARRA-Gym for Evaluating Interactive Narrative Agents](https://mlsanity.com/paper/2605.08503/) (2026)
- [LitVISTA: A Benchmark for Narrative Orchestration in Literary Text](https://mlsanity.com/paper/2601.06445/) (2026)
- [FocalLens: Visualizing Narratives through Focalization](https://mlsanity.com/paper/2604.14456/) (2026)
- [The Mathematical Collective Unconscious: Jungian Archetypes within the Latent Space](https://mlsanity.com/paper/2610.15678/) (2026)

## Abstract (arXiv)

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.