@inproceedings{wang-etal-2025-towards-novel,
title = "Towards A ``Novel'' Benchmark: Evaluating Literary Fiction with Large Language Models",
author = "Wang, Wenqing and
Gao, Mingqi and
Hu, Xinyu and
Wan, Xiaojun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1114/",
doi = "10.18653/v1/2025.findings-acl.1114",
pages = "21648--21673",
ISBN = "979-8-89176-256-5",
abstract = "Current exploration on creative generation focuses mainly on short stories, poetry, and scripts. With the expansion of Large Language Models (LLMs) context windows, ``novel'' avenues emerge. This study aims to extend the boundaries of Natural Language Generation (NLG) evaluation by exploring LLMs' capabilities in more challenging long-form fiction. We propose a new multi-level evaluation framework that incorporates ten metrics across the Macro, Meso, and Micro levels. An annotated fiction dataset, sourced from human authors, LLMs, and human-AI collaborations in both English and Chinese is then constructed. Human evaluation reveals notable disparities between LLM-generated and human-authored fictions, particularly the ``high-starting, low-ending'' pattern in LLM outputs. We further probe ten high-performing LLMs through different prompt templates, achieving moderate correlations by strategically utilizing diverse LLMs tailored to different levels, as an initial step towards better automatic fiction evaluation. Finally, we offer a fine-grained analysis of LLMs capabilities through six issues, providing promising insights for future advancements."
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<abstract>Current exploration on creative generation focuses mainly on short stories, poetry, and scripts. With the expansion of Large Language Models (LLMs) context windows, “novel” avenues emerge. This study aims to extend the boundaries of Natural Language Generation (NLG) evaluation by exploring LLMs’ capabilities in more challenging long-form fiction. We propose a new multi-level evaluation framework that incorporates ten metrics across the Macro, Meso, and Micro levels. An annotated fiction dataset, sourced from human authors, LLMs, and human-AI collaborations in both English and Chinese is then constructed. Human evaluation reveals notable disparities between LLM-generated and human-authored fictions, particularly the “high-starting, low-ending” pattern in LLM outputs. We further probe ten high-performing LLMs through different prompt templates, achieving moderate correlations by strategically utilizing diverse LLMs tailored to different levels, as an initial step towards better automatic fiction evaluation. Finally, we offer a fine-grained analysis of LLMs capabilities through six issues, providing promising insights for future advancements.</abstract>
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%0 Conference Proceedings
%T Towards A “Novel” Benchmark: Evaluating Literary Fiction with Large Language Models
%A Wang, Wenqing
%A Gao, Mingqi
%A Hu, Xinyu
%A Wan, Xiaojun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-towards-novel
%X Current exploration on creative generation focuses mainly on short stories, poetry, and scripts. With the expansion of Large Language Models (LLMs) context windows, “novel” avenues emerge. This study aims to extend the boundaries of Natural Language Generation (NLG) evaluation by exploring LLMs’ capabilities in more challenging long-form fiction. We propose a new multi-level evaluation framework that incorporates ten metrics across the Macro, Meso, and Micro levels. An annotated fiction dataset, sourced from human authors, LLMs, and human-AI collaborations in both English and Chinese is then constructed. Human evaluation reveals notable disparities between LLM-generated and human-authored fictions, particularly the “high-starting, low-ending” pattern in LLM outputs. We further probe ten high-performing LLMs through different prompt templates, achieving moderate correlations by strategically utilizing diverse LLMs tailored to different levels, as an initial step towards better automatic fiction evaluation. Finally, we offer a fine-grained analysis of LLMs capabilities through six issues, providing promising insights for future advancements.
%R 10.18653/v1/2025.findings-acl.1114
%U https://aclanthology.org/2025.findings-acl.1114/
%U https://doi.org/10.18653/v1/2025.findings-acl.1114
%P 21648-21673
Markdown (Informal)
[Towards A “Novel” Benchmark: Evaluating Literary Fiction with Large Language Models](https://aclanthology.org/2025.findings-acl.1114/) (Wang et al., Findings 2025)
ACL