Researchers model how AI and humans time new words in text
A new paper on arXiv explores how information appears over time in natural language. The authors propose using neural temporal point processes to predict when new words or high-surprisal tokens appear in text sequences.
Key points
- Researchers use neural temporal point processes to model timing of new words and high-surprisal tokens in text
- AI models generate new words faster than humans but share a decaying intensity pattern for both
- Study suggests AI and human language production differ in constrained context representation
The study finds that AI models introduce new words at a much faster rate than humans, but both produce a decaying pattern of intensity over time for these events. However, high-surprisal tokens—words that are unexpected—do not show this difference between humans and AI. The findings challenge and expand earlier research, offering new insights into how language generation differs between humans and AI systems.
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