arXiv AI June 18, 2026 By David Y. Liu, Aditya Joshi, Paul Dawson

Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey

arXiv:2602. 15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks.

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arXiv Computation and Language

Sep 11

Characterizing Narrative Content in Web-scale LLM Pretraining Data

The paper presents a detailed examination of narrative elements—agency, setting, and events—within the Dolma web-scale pretraining corpus. Using a framework of 11 interpretable dimensions, the authors hand‑annotated 400 passages, expanded this to a 25,000‑passage LLM‑labeled dataset, and trained NarraBERT models to predict narrative features across 13 million passages, producing the NarraDolma dataset. The study reveals that narrative structure is measurable at scale and that narrative qualities vary unevenly across different data sources, topics, and formats, highlighting gaps in current data curation practices.

By Teagan Johnson, Elliott Ash, Andrew Piper, Maria Antoniak

llms

arXiv AI

Jun 6

Narrative Knowledge Weaver: Narrative-Centric Retrieval-Augmented Reasoning for Long-Form Text Understanding

arXiv:2606. 05724v1 Announce Type: cross Abstract: Long-form narrative QA requires reasoning over evolving story worlds rather than isolated passages: answers may depend on earlier goals, changing character states, social relations, causal triggers, temporal position, and later consequences.

By Qiuyu Tian, Fengyi Chen, Yiding Li, Youyong Kong, Fan Guo, Yuyao Li, Jinjing Shen, Zhijing Xie, Yiyun Luo, Xin Zhang, Yingce Xia, Zequn Liu

rag benchmarks

arXiv Computation and Language

3d ago

Evaluating Whether LLMs Can Reliably Connect the DOTs?

arXiv:2609.38406v1 Announce Type: new Abstract: Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct mi...

By Eftekhar Hossain, John Salvador, Santu Karmaker

llms benchmarks

arXiv AI

Jun 18

Retell, Reward, Repeat: Reinforcement Learning for Narrative Theory-Informed Story Retelling

arXiv:2601. 17226v2 Announce Type: replace-cross Abstract: Counterfactual story retelling exposes LLM shortcomings in constrained narrative solution spaces where they can no longer rely on recalling memorised training data.

By David Y. Liu, Xanthe Muston, Dipankar Srirag, Aditya Joshi, Sebastian Sequoiah-Grayson

llms nlp reinforcement-learning fine-tuning

arXiv AI

Jun 29

Narrative-UFET: Narrative Generation for Ultra-Fine Entity Typing

arXiv:2606. 27598v1 Announce Type: cross Abstract: Ultra-fine entity typing (UFET) assigns highly specific types to entity mentions, but current approaches struggle with types in the long tail.

By Mreedul Gupta, Advait Deshmukh, Ashwin Umadi, Matt Pauk, Maria Leonor Pacheco

arXiv AI

Sep 10

LitSeg: Narrative-Aware Document Segmentation for Literary RAG

arXiv:2605.27156v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, particularly for long-tail do...

By Ruikang Zhang, Zhanni Chen, Yiqiao Cai, Qi Su

llms rag computer-vision fine-tuning efficiency benchmarks

One email a morning, machine-written

Related stories

Characterizing Narrative Content in Web-scale LLM Pretraining Data

Narrative Knowledge Weaver: Narrative-Centric Retrieval-Augmented Reasoning for Long-Form Text Understanding

Evaluating Whether LLMs Can Reliably Connect the DOTs?

Retell, Reward, Repeat: Reinforcement Learning for Narrative Theory-Informed Story Retelling

Narrative-UFET: Narrative Generation for Ultra-Fine Entity Typing

LitSeg: Narrative-Aware Document Segmentation for Literary RAG