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A preliminary analysis of LLM creative outputs spanning three years of model releases shows a statistically significant decrease in model output diversity over time, suggesting that LLM outputs may be converging in creative substance across models.
This study studies how large language model (LLM)-powered chatbots respond to series of questions about legal reasonableness, and suggests that chatbot responses generally track those of human participants.
This paper proposes a novel technique for accurately and automatically inferring LLM-related scrapers and demonstrates that the approach can reliably identify which scrapers feed which LLM, including several that are not publicly known or disclosed by the companies.
A human-grounded framework that uses the empirical distribution of human writing on a topic to measure the distributional breadth of LLM-generated content on that same topic, and proposes two metrics, LLM Coverage (LLM-Cov) and In-Boundary Rate (IBR), that separate the plausibility of LLM content from its distributiona...
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