Exploring Context Window of Large Language Models via Decomposed Positional Vectors

TL;DR

This paper investigates how positional vectors influence large language models' performance beyond their context window, proposing two training-free methods to extend the context window effectively.

Contribution

It introduces a mean-based decomposition method to analyze positional vectors and develops two novel, training-free techniques for context window extension.

Findings

Positional vectors significantly impact LLM attention beyond the context window.

The proposed methods effectively extend the context window length.

Experimental results demonstrate improved performance with the new extension techniques.

Abstract

Transformer-based large language models (LLMs) typically have a limited context window, resulting in significant performance degradation when processing text beyond the length of the context window. Extensive studies have been proposed to extend the context window and achieve length extrapolation of LLMs, but there is still a lack of in-depth interpretation of these approaches. In this study, we explore the positional information within and beyond the context window for deciphering the underlying mechanism of LLMs. By using a mean-based decomposition method, we disentangle positional vectors from hidden states of LLMs and analyze their formation and effect on attention. Furthermore, when texts exceed the context window, we analyze the change of positional vectors in two settings, i.e., direct extrapolation and context window extension. Based on our findings, we design two training-free context window extension methods, positional vector replacement and attention window extension. Experimental results show that our methods can effectively extend the context window length.