English(EN) Can LLMs Tell Stories Out of Order? A Corpus and Benchmark for Nonlinear Narrative Generation

新基准测试大型语言模型生成非线性叙事能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一个新的基准测试，用于评估大型语言模型生成非线性叙事的能力，即故事中事件的顺序与这些事件的实际发生顺序不同。该基准测试包含83部已出版的作品，并标注了事件顺序、因果关系和角色知识。测试结果显示，19个语言模型倾向于比原作更按时间顺序讲故事，而人类读者发现模型生成的乱序故事更难理解。 AI

影响这项研究突显了大型语言模型在理解和生成复杂叙事结构方面的现有局限性，表明需要进一步开发以实现更复杂的讲故事能力。

排序理由该集群包含一篇详细介绍评估大型语言模型能力的新基准测试的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 22 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍评估大型语言模型能力的新基准测试的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Syed Rifat Raiyan, Md. Tariquzzaman, Md Farhan Ishmam, Tenghao Huang · 2026-10-16 04:00

大型语言模型能讲乱序故事吗？一个非线性叙事生成语料库和基准测试

arXiv:2610.21468v1 Announce Type: cross Abstract: Every story has two orders: the order in which its events happen and the order in which they are told. A story is nonlinear when the two differ. We introduce a benchmark of 83 published works that measures whether language models …

本文如何被排名

报道来源 [1]

大型语言模型能讲乱序故事吗？一个非线性叙事生成语料库和基准测试

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