LLMs exhibit temporal flattening with substantially reduced semantic and cognitive-emotional drift compared to humans, allowing 94% accurate distinction from variability patterns alone.
InProceedings of the 54th Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 1489–1501, Berlin, Germany
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No single temporal tokenization strategy is best for all event data; performance depends on matching the tokenizer to the statistical shape of the data.
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Temporal Flattening in LLM-Generated Text: Comparing Human and LLM Writing Trajectories
LLMs exhibit temporal flattening with substantially reduced semantic and cognitive-emotional drift compared to humans, allowing 94% accurate distinction from variability patterns alone.
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Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models
No single temporal tokenization strategy is best for all event data; performance depends on matching the tokenizer to the statistical shape of the data.