arXiv:2606.17350v1 Announce Type: new 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 the diversity of LLM-generated stories through the framework of narrative similarity. Using a contrastive framework and a dataset of human-written stories and prompts from r/WritingPrompts, we collect narrative similarity judgments across 10 representative LLMs, utilizing both human evaluations and three different automa
The proliferation of advanced LLMs and their growing adoption in creative and content generation roles necessitates a deeper understanding of their output diversity, especially as their capabilities mature.
Understanding the diversity of LLM-generated content is critical for evaluating their utility in various applications, from creative industries to information dissemination, and for identifying potential biases or homogenization risks.
The focus shifts beyond mere generation quality to the nuanced aspect of output diversity, influencing how LLMs are developed, assessed, and deployed across different sectors.
- · AI researchers focusing on diversity metrics
- · Developers of custom/fine-tuned LLMs
- · Platforms providing diverse LLM outputs
- · Generic, undifferentiated LLM providers
- · Content creators relying solely on basic LLM outputs
- · Users expecting inherent diversity without specific prompting
Further research into controlling and enhancing output diversity in large language models will become a priority.
New metrics and benchmarks for evaluating narrative diversity will emerge, becoming standard in LLM development and deployment.
The development of 'diversity-aware' LLM architectures or training methodologies could lead to a new generation of more versatile AI-powered creative tools.
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