The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey

TL;DR

This survey comprehensively reviews techniques for extending context length in Large Language Models, discussing challenges, evaluation methods, and future directions to improve NLP applications.

Contribution

It provides an organized overview of existing context extension strategies, analyzes evaluation standards, and highlights open challenges in the field.

Findings

Identifies key challenges in context length extension

Summarizes current strategies and their effectiveness

Highlights need for standardized evaluation methods

Abstract

The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP), contributing to substantial progress in both text comprehension and generation. However, amidst these advancements, it is noteworthy that LLMs often face a limitation in terms of context length extrapolation. Understanding and extending the context length for LLMs is crucial in enhancing their performance across various NLP applications. In this survey paper, we delve into the multifaceted aspects of exploring why it is essential, and the potential transformations that superior techniques could bring to NLP applications. We study the inherent challenges associated with extending context length and present an organized overview of the existing strategies employed by researchers. Additionally, we discuss the intricacies of evaluating context extension techniques and highlight the open challenges that researchers face in this domain. Furthermore, we explore whether there is a consensus within the research community regarding evaluation standards and identify areas where further agreement is needed. This comprehensive survey aims to serve as a valuable resource for researchers, guiding them through the nuances of context length extension techniques and fostering discussions on future advancements in this evolving field.