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Modifying large language model post-training for diverse creative writing

John Joon Young Chung

Computer 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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creativity frameworks › creative-textual creativityevaluation › document-levelevaluation › automatic metricsmodel used › Medium (8-24)related to creativity › related to creativity as a textual genretextual genre › literaturescope › creative trainingscope › technical research

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