Modeling the Arrival Time of Information-Bearing Events in Natural Language
Understanding how information is distributed over time is fundamental to modeling natural language. While existing empirical studies primarily rely on post-hoc analysis of information-theoretic metrics, such as surprisal, little is known about the generation processes under the surface.
This research introduces a neural temporal point process (TPP) model to analyze the timing of information-bearing events in natural language, comparing human and large language model (LLM) generation patterns.
What happened
The study developed a model using neural temporal point processes to predict the arrival times of information-bearing events in text. It analyzed differences in generation patterns between humans and LLMs, focusing on new-word and high-surprisal-token events. Experimental results revealed distinct intensity patterns and generation rates.
Why it matters
Understanding temporal dynamics in language generation helps improve NLP models, provides insights into human language production constraints, and reveals differences between human and machine-generated text patterns.
- LLMs exhibit distinct generation patterns compared to humans in new-word frequency
- Temporal intensity patterns differ between event types (new-words vs. high-surprisal tokens)
- Neural TPPs offer a novel method for analyzing sequential text dynamics
- Human and machine generation show both similarities and differences in information distribution
The bigger picture
The findings contribute to better modeling of language generation processes and provide a framework for analyzing temporal patterns in text, which could inform future NLP system design and evaluation.
This research advances understanding of information timing in language by introducing neural TPPs, revealing key differences between human and LLM generation patterns, and providing new insights into temporal dynamics of natural language.
Technical details
- Neural temporal point processes (TPPs) for event time prediction
- Contextual semantic representations from LLMs as input features
- Event intensity functions for modeling generation rates
- Surprisal metrics for identifying high-information events
FAQ
What is a neural temporal point process (TPP)?
A mathematical framework for modeling the timing of events in sequences, used here to predict information-bearing events in text.
How do LLMs differ from humans in text generation according to this study?
LLMs produce new words at higher rates than humans, but show similar patterns for high-surprisal tokens. Both exhibit decaying intensity for new-word events over time.
What are information-bearing events in this context?
Events that carry significant information, specifically new-word occurrences and high-surprisal tokens identified through information-theoretic metrics.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.CL (NLP) on Oct 14, 2026.
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