Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A systematic empirical study by Zefang Liu, Nam H Nguyen, Yinzhu Quan, and Shi-Xiong Zhang investigates temporal tokenization strategies for modeling event sequences with large language models (LLMs). The research compares five distinct encoding strategies: naive numeric strings, high-precision byte-level representations, human-semantic calendar tokens, classic uniform binning, and adaptive residual scalar quantization. These strategies were evaluated by fine-tuning LLMs on real-world datasets exhibiting diverse statistical distributions, from smooth log-normal to discrete, spiky patterns. The analysis concludes that no single strategy is universally superior; instead, prediction performance critically depends on aligning the tokenizer with the data's specific statistical properties, highlighting temporal tokenization as a crucial, often overlooked, design dimension in LLM-based event modeling.

Key takeaway

For machine learning engineers developing LLM-based event sequence models, your choice of temporal tokenization strategy is paramount. Do not assume a one-size-fits-all solution; instead, carefully analyze your event data's statistical distribution before selecting an encoding method. Aligning the tokenizer with your data's properties will directly impact prediction performance and model effectiveness, making this an essential design decision for robust temporal modeling.

Key insights

Optimal temporal tokenization for LLM event sequence modeling depends on data's statistical distribution.

Principles

• Temporal tokenization is a critical design dimension.
• No single tokenization strategy is universally superior.
• Align tokenizer with data's statistical properties.

Method

Systematic empirical study comparing five temporal encoding strategies: numeric strings, byte-level, calendar, uniform binning, and adaptive residual scalar quantization, evaluated by fine-tuning LLMs on diverse real-world datasets.

In practice

• Evaluate multiple tokenization strategies.
• Analyze data's statistical distribution first.
• Consider adaptive quantization for complex data.

Topics

• Temporal Tokenization
• Event Sequence Modeling
• Large Language Models
• Data Distribution
• Residual Scalar Quantization
• Byte-level Representations

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.