StoryLens creates a benchmark and models for context-enriched story rewriting that better matches reader preferences than style transfer alone.
Word2world: Generating stories and worlds through large language models
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A pipeline combining LLMs, fine-tuned embeddings, and diffusion models generates personalized Pokémon cards; a 49-participant user study found high satisfaction and that most users realized their ideas via prompt tweaks.
A dependency-aware prompt pipeline with structured JSON intermediates produces coherent, scalable RPG worlds and quests from LLMs.
citing papers explorer
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StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment
StoryLens creates a benchmark and models for context-enriched story rewriting that better matches reader preferences than style transfer alone.
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From LLM-Driven Trading Card Generation to Procedural Relatedness: A Pok\'emon Case Study
A pipeline combining LLMs, fine-tuned embeddings, and diffusion models generates personalized Pokémon cards; a 49-participant user study found high satisfaction and that most users realized their ideas via prompt tweaks.
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From World-Gen to Quest-Line: A Dependency-Driven Prompt Pipeline for Coherent RPG Generation
A dependency-aware prompt pipeline with structured JSON intermediates produces coherent, scalable RPG worlds and quests from LLMs.