arXiv:2606.13624v1 Announce Type: new Abstract: Large language models (LLMs) have enabled time series (TS) analysis by jointly modeling numerical observations and textual context through a shared token interface. However, TS tokens and prompt tokens exhibit fundamentally different information structures, making uniform token processing inefficient. In this paper, we study token efficiency in TS language modeling from an asymmetric-token perspective. We show that TS tokens have highly uneven spectral contributions, where many tokens share redundant frequency patterns while a small subset preser
The proliferation of Large Language Models (LLMs) into diverse domains like time series analysis is exposing fundamental inefficiencies in their underlying tokenization and processing mechanisms, demanding new research into optimization.
Improving token efficiency for time series data in LLMs can significantly reduce computational overhead and enhance accuracy, making these models more viable for critical applications in finance, science, and industry.
This research suggests a move away from uniform token processing to adaptive compression, which could lead to more specialized and efficient LLMs for non-textual data types, challenging the current one-size-fits-all approach.
- · AI researchers
- · Time series analytics platforms
- · LLM developers focusing on efficiency
- · Industries reliant on time series data (finance, manufacturing)
- · Generic LLM architectures ignoring data specifics
- · Companies with inefficient AI infrastructure
- · Developers stuck with traditional tokenization methods
Adaptive compression techniques will be integrated into next-generation LLMs, improving their performance on time series data.
This optimization could accelerate the adoption of LLM-based solutions in areas with high-frequency numerical data, such as real-time trading or industrial control.
Increased efficiency could lead to smaller, more specialized LLMs capable of running on edge devices for time-series analysis, decentralizing AI capabilities.
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Read at arXiv cs.CL