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Improving Diversity in LLM Short Story Generation

Paper recorded by Signals 4 on 2026-10-05 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-05 on arXiv · recorded by Signals 4 on 2026-10-06

Category: cs.CL · 自然语言处理 · first seen 2026-10-06

Abstract

Large language models (LLMs) can generate accurate responses, but these are void of diversity. We attempt to address this for the task of creative short story generation. Drawing on established writing conventions and known LLM limitations, we target variation in genre, tone, style, and named entities. To promote diversity across these dimensions, we introduce DivLM, an LLM post-training framework

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#323 most recent of 660 cs.CL papers we have recorded · ↑ newer: ufakzeka-karar: An Open Turkish Typed-Decision Model with Order-Invari · ↓ older: Domain adaptation of Russian ModernBERT for long legal documents
Cite this page: Improving Diversity in LLM Short Story Generation: the #323 most recent of 660 cs.CL papers we have recorded (as of 2026-10-05). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/improving-diversity-in-llm-short-story-generation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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