SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs

2026-04-10 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

SepSeq is a novel, training-free framework designed to enhance Large Language Models' (LLMs) ability to process long numerical sequences, addressing performance degradation caused by attention dispersion within the Softmax mechanism. This plug-and-play solution strategically inserts separator tokens into sequences, which function as attention sinks. This recalibrates the LLM's attention to concentrate on local segments while simultaneously maintaining global contextual understanding. Evaluated across 9 widely-adopted LLMs, SepSeq demonstrated an average relative accuracy improvement of 35.6% across various domains. Furthermore, the framework achieved an average reduction of 16.4% in total inference token consumption, making it both effective and efficient for numerical sequence processing.

Key takeaway

For AI Engineers deploying LLMs for tasks involving extensive numerical data, integrating SepSeq can significantly boost accuracy and reduce operational costs. You should consider implementing this training-free framework to overcome common performance bottlenecks in long numerical sequence processing, especially when working with models that struggle with attention dispersion. This approach offers a direct path to improved model reliability and efficiency without requiring retraining.

Key insights

SepSeq improves LLM processing of long numerical sequences by using separator tokens to manage attention dispersion.

Principles

• Attention dispersion degrades LLM performance on long numerical sequences.
• Separator tokens can act as attention sinks to recalibrate focus.

Method

SepSeq mitigates attention dispersion in LLMs by strategically inserting separator tokens into long numerical sequences, enabling local focus while preserving global context.

In practice

• Apply SepSeq to improve LLM accuracy on numerical tasks.
• Reduce LLM inference token consumption for long sequences.

Topics

• SepSeq
• Large Language Models
• Numerical Sequence Processing
• Attention Dispersion
• Softmax Mechanism

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.