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LLMs Produce Homogeneous Stories, Lacking Human Narrative Diversity

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv investigates the narrative diversity of large language models (LLMs) when generating stories. The study found that LLM-generated narratives are significantly more similar to each other than human-written stories. This homogeneity is particularly pronounced in frontier models, which tend to converge on a generic narrative style. The research also indicates that common methods like negative prompting and temperature scaling are ineffective at increasing the diversity of LLM outputs. AI

IMPACT LLMs struggle to produce diverse narratives, potentially limiting creative applications and highlighting a gap compared to human authors.

RANK_REASON Research paper published on arXiv detailing findings about LLM narrative diversity.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs Produce Homogeneous Stories, Lacking Human Narrative Diversity

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Thennal DK, Hans Ole Hatzel ·

    Do Large Language Models Always Tell The Same Stories?

    arXiv:2606.17350v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested. In this work, we investigate …

  2. arXiv cs.CL TIER_1 English(EN) · Hans Ole Hatzel ·

    Do Large Language Models Always Tell The Same Stories?

    Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested. In this work, we investigate the diversity of LLM-generated stories through the…

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