标题： AI中的回声：量化大型语言模型输出中情节多样性的缺失 显示英文标题

标题： Echoes in AI: Quantifying Lack of Plot Diversity in LLM Outputs

摘要： 随着大型语言模型（LLMs）的快速发展，LLMs在创意内容构思和生成中的应用日益增多。 一个关键问题浮现：当前的LLMs能否提供足够多样的想法，真正增强集体创造力？ 我们对两种最先进的LLMs，GPT-4和LLaMA-3，在故事生成方面的表现进行了研究，并发现 LLM生成的故事通常包含在多次生成中反复出现的情节元素。 为了量化这一现象，我们引入了Sui Generis分数，该分数估计一个情节元素在由同一LLM生成的其他故事情节中出现的可能性有多大。 在100个短故事上进行评估，我们发现LLM生成的故事经常包含在多次生成中频繁重复的独特情节元素，而原始的人类写作故事很少被重新创作，甚至很少被部分重复。 此外，我们的用户评估显示，故事片段之间的Sui Generis分数排名与人类对惊喜程度的判断存在中等程度的相关性，尽管分数计算是完全自动的，不依赖于人类判断。 显示英文摘要

摘要： With rapid advances in large language models (LLMs), there has been an increasing application of LLMs in creative content ideation and generation. A critical question emerges: can current LLMs provide ideas that are diverse enough to truly bolster the collective creativity? We examine two state-of-the-art LLMs, GPT-4 and LLaMA-3, on story generation and discover that LLM-generated stories often consist of plot elements that are echoed across a number of generations. To quantify this phenomenon, we introduce the Sui Generis score, which estimates how unlikely a plot element is to appear in alternative storylines generated by the same LLM. Evaluating on 100 short stories, we find that LLM-generated stories often contain combinations of idiosyncratic plot elements echoed frequently across generations, while the original human-written stories are rarely recreated or even echoed in pieces. Moreover, our human evaluation shows that the ranking of Sui Generis scores among story segments correlates moderately with human judgment of surprise level, even though score computation is completely automatic without relying on human judgment.