arXiv:2605.26492v1 Announce Type: cross Abstract: LLM-generated stories are a popular use case, but they show very low variability. We sample 20,000 total stories from four current models using five prompts. We find that 11 words occur in 88.3% of generated stories, with little difference between models. These words include names (Elias, Mara, Elara), settings (lighthouses), and professions (clockmaker, librarian). These tokens do not often occur in published literature nor pre-training data, but they are found in preference data that is likely to have been used by all current models. Surprisi
The proliferation of LLMs makes their output quality and characteristics a critical area of study, particularly as they are integrated into more applications.
This finding highlights a significant limitation in current LLM generation diversity, which could undermine widespread adoption for creative or nuanced tasks.
Understanding the origins of LLM's low diversity points to an issue with preference data, requiring adjustments in training and fine-tuning methodologies.
- · AI researchers focused on prompt engineering
- · Developers of diverse preference datasets
- · Specialized content creators
- · LLM providers relying on current preference datasets
- · Generative AI applications requiring high variability
- · Generic storytelling platforms
Ongoing research into LLM biases and limitations will intensify, prompting calls for more transparent and diverse training practices.
The market for specialized, domain-specific LLMs or fine-tuning services that overcome generic stylistic patterns will likely grow.
New evaluation metrics beyond perplexity or human preference will emerge to assess the true originality and breadth of LLM outputs.
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Read at arXiv cs.LG