arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.
By Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin
arXiv:2607. 22632v1 Announce Type: new Abstract: The rapid rise of vlogs as a personalized storytelling medium has created a demand for automated systems to evaluate and refine vlog editing plans.
By Yexiang Liu, Wen Zhong, Sijie Zhu, Xin Gu, Fan Chen, Junxian Duan, Jie Cao, Longyin Wen, Zhenfang Chen
arXiv:2609.14677v1 Announce Type: new
Abstract: Large language models (LLMs) have changed the way people engage with stories. Drawing on public chatbot logs, we can see that when users generate stori...
By Advait Deshmukh, Nora Benedict, Melanie Walsh, Maria Antoniak
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.
By Logan Mann, Abdur Rahman, Mohammad Saifullah, Taaha Kazi, Vasu Sharma
arXiv:2607. 19038v1 Announce Type: cross Abstract: Translating novels into films poses a grand challenge for generative artificial intelligence, requiring conversion of abstract literary prose into long-form, multi-scene visual narratives.
By Jialong Zuo, Haotong Zuo, Shiwei Zhang, Xiang Wang, Chen Li, Nong Sang, Changxin Gao, Xiang Bai
arXiv:2605. 17064v2 Announce Type: replace Abstract: Large language models are optimized for instruction following and agentic tasks remain poorly aligned with the requirements of high-quality creative writing.
By Jan Zierstek, Matteo Batelic, Maya Medjad, Tim Sch\"onenberger
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.
By Mingzhe Lu, Yanbing Liu, Jiayue Wu, Jiarui Zhang, Qihao Wang, Yue Hu, Yunpeng Li, Yangyan Xu
arXiv:2609.38406v1 Announce Type: new
Abstract: Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct mi...
By Eftekhar Hossain, John Salvador, Santu Karmaker
arXiv:2606. 13348v1 Announce Type: cross Abstract: Computational creativity in Interactive Fiction faces a fundamental tension: Large Language Models (LLM) may produce creative narratives but struggle with world coherence, while symbolic systems ensure consistency but lack creative flexibility.
By Micaela Vaucher, Santiago Silveira, Santiago G\'ongora, Luis Chiruzzo
arXiv:2608. 15654v1 Announce Type: cross Abstract: Large language models can write fluent stories, but open-ended storytelling requires more than local fluency.
By Yuqi Chen, Sixuan Li, Yunfeng Cai, Xueai Li, Ka Man Yan, Ying Li
arXiv:2608. 12336v1 Announce Type: cross Abstract: A story premise is the creative spark from which a full narrative can grow.
By Yang Yang, Zining Zhong, Qian Cao, Jindong Li, Boyun Xu, Kaishen Yuan, Menglin Yang, Yutao Yue
The paper examines whether existing automatic methods can reliably assess creativity in text produced by large language models (LLMs). By collecting human ratings on 11 creativity dimensions for both human and AI short stories, the authors compare these judgments with automated metrics and LLM-as-a-Judge evaluations. The results show a significant misalignment: automated metrics and LLM judges favor AI-generated stories and show near-zero correlation with human assessments, revealing fundamental limitations in current computational approaches to evaluating creative text.
By Alessandro Tutone, Giorgio Franceschelli, Mirco Musolesi