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The advent of Large Language Models (LLMs) represents a notable breakthroughin Natural Language Processing (NLP), contributing to substantial progress inboth text comprehension and generation. However, amidst these advancements, itis noteworthy that LLMs often face a limitation in terms of context lengthextrapolation. Understanding and extending the context length for LLMs iscrucial in enhancing their performance across various NLP applications. In thissurvey paper, we delve into the multifaceted aspects of exploring why it isessential, and the potential transformations that superior techniques couldbring to NLP applications. We study the inherent challenges associated withextending context length and present an organized overview of the existingstrategies employed by researchers. Additionally, we discuss the intricacies ofevaluating context extension techniques and highlight the open challenges thatresearchers face in this domain. Furthermore, we explore whether there is aconsensus within the research community regarding evaluation standards andidentify areas where further agreement is needed. This comprehensive surveyaims to serve as a valuable resource for researchers, guiding them through thenuances of context length extension techniques and fostering discussions onfuture advancements in this evolving field.

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