Hugging Face Trending Papers
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing
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arXiv Computation and Language
Oct 15
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing
CSWBench is a benchmark designed to evaluate context-sensitive code-switching in creative writing, featuring two settings: Focused Narrative and Extended Narrative. The accompanying CSWEval framework assesses event-level aspects such as Licensing, Contextual Appropriateness, and Functional Gain, as well as whole-text quality metrics tailored to each narrative setting. Experiments with twelve large language models reveal that while models show some code-switching competence, performance varies significantly across narrative contexts, with stronger challenges in multilingual literary integration and clearer associations between narratively salient switches and contextual/functional realization.
arXiv AI
Jul 2
Controllable Narrative Rendering for Enhanced Assisted Writing
arXiv:2607. 00009v1 Announce Type: cross Abstract: Despite the remarkable proficiency of large language models (LLMs) in basic writing assistance, their utility in creative writing is fundamentally hindered by a persistent binary failure.
arXiv AI
Jun 17
NarrativeWorldBench: A Frontier-Saturated Benchmark and a Latent World Model for Long-Horizon Co-Creative Audio Drama
arXiv:2606. 17391v1 Announce Type: cross Abstract: Long-form serialized audio drama, with arcs that run for 200 to 800 episodes, is a major creative medium and a setting where frontier large language models (LLMs) fail.
arXiv AI
Jun 18
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.
arXiv AI
Jul 22
PlotTwist: A Creative Plot Generation Framework with Small Language Models
arXiv:2603. 16410v2 Announce Type: replace-cross Abstract: Creative plot generation presents a fundamental challenge for language models: transforming a concise premise into a coherent narrative that sustains global coherence, character development, pacing, tone consistency, and emotional progression.
arXiv Computation and Language
Sep 3
How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling
The paper investigates how Large Language Models (LLMs) construct fictional worlds, specifically examining setting as a measurable aspect of storyworld creation. By generating 1,000 AI stories per model in English and German and comparing them to human-authored fiction from Project Gutenberg, the authors classify narrative space into five categories—action, perceived, visual, descriptive, and no space—using fine‑tuned BERT classifiers. Results show that human texts mainly use action space, grounding narratives in character-environment interaction, while LLMs consistently overproduce perceived space, focusing on atmosphere and affect, with this pattern varying by model and language.