# Assessing Language Models' Worldview for Fiction Generation

arXiv: https://arxiv.org/abs/2408.07904 · HTML: https://mlsanity.com/paper/2408.07904/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [A Study on Large Language Models' Limitations in Multiple-Choice Question Answering](https://mlsanity.com/paper/2401.07955/) (2024)
- [SWAG: Storytelling With Action Guidance](https://mlsanity.com/paper/2402.03483/) (2024)
- [TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability](https://mlsanity.com/paper/2406.01855/) (2024)
- [A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation](https://mlsanity.com/paper/2311.11271/) (2023)

## Built on by (most cited first, 3 of 3)

- [SCORE: Story Coherence and Retrieval Enhancement for AI Narratives](https://mlsanity.com/paper/2503.23512/) (2025)
- [Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberation](https://mlsanity.com/paper/2509.14760/) (2025)
- [HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing](https://mlsanity.com/paper/2604.19071/) (2026)

## Abstract (arXiv)

The use of Large Language Models (LLMs) has become ubiquitous, with abundant applications in computational creativity. One such application is fictional story generation. Fiction is a narrative that occurs in a story world that is slightly different than ours. With LLMs becoming writing partners, we question how suitable they are to generate fiction. This study investigates the ability of LLMs to maintain a state of world essential to generate fiction. Through a series of questions to nine LLMs, we find that only two models exhibit consistent worldview, while the rest are self-conflicting. Subsequent analysis of stories generated by four models revealed a strikingly uniform narrative pattern. This uniformity across models further suggests a lack of `state' necessary for fiction. We highlight the limitations of current LLMs in fiction writing and advocate for future research to test and create story worlds for LLMs to reside in. All code, dataset, and the generated responses can be found in https://github.com/tanny411/llm-reliability-and-consistency-evaluation.