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Modifying large language model post-training for diverse creative writing

John Joon Young Chung

2025Computer science - computation and language, computer science - machine learning

Abstract

As creative writing tasks do not have singular correct answers, large language models (LLMs) trained to perform these tasks should be able to generate diverse valid outputs. However, LLM post-training often focuses on improving generation quality but neglects to facilitate output diversity. Hence, in creative writing generation, we investigate post-training approaches to promote both output diversity and quality. Our core idea is to include deviation – the degree of difference between a trainin

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Human-In-The-Loop › Autonomous GenerationCreativity Frameworks › Linguistic CreativityLevel of Analysis › Document-LevelCreativity Evaluation Methods › Automatic MetricsModel Scale › Medium (8-24)Relationship to Creativity › ImplicitTextual Domain › Literary TextsResearch Focus › Creativity-Oriented TrainingResearch Focus › Architectural ResearchResearch Focus › Fine-tuning

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Paper ID: 23d098cf-115a-49ef-bded-fe82097a366eAdded: 10/26/2025