---
title: "Context Length Extension Techniques in LLMs — arXiv Survey"
url: https://stacklist.com/card/a2e80df2-d29a-4456-8fa3-6f98bc2783cc
source_url: "https://arxiv.org/abs/2401.07872"
stack: https://stacklist.com/c/technology/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e
summary: "This survey paper explores context length extension techniques in Large Language Models, examining why extending context length is essential, the inherent challenges, and existing strategies employed by researchers. It provides an organized overview of evaluation methods, highlights open challenges, and discusses the lack of consensus on evaluation standards within the research community."
tags: "context-length-extension, large-language-models, natural-language-processing, survey, transformer-architecture, text-comprehension, evaluation-standards"
key_entities: "Saurav Pawar (person), S.M Towhidul Islam Tonmoy (person), S M Mehedi Zaman (person), Vinija Jain (person), Aman Chadha (person), Amitava Das (person), Large Language Models (technology), Context Length Extension (concept), Natural Language Processing (concept), arXiv (organization)"
classification: "reference"
content_hash: "sha256:3e42c0dc13066e1a9ffc459ba02238aeafeb447fecee926e24a54dcddd22d17a"
acp_version: "0.2"
token_counts_approximate: 1119
visibility: public
agent_accessible: true
status: "final"
---

# Context Length Extension Techniques in LLMs — arXiv Survey

--> Computer Science > Computation and Language arXiv:2401.07872 (cs) [Submitted on 15 Jan 2024] Title: The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey Authors: Saurav Pawar , S.M Towhidul Islam Tonmoy , S M Mehedi Zaman , Vinija Jain , Aman Chadha , Amitava Das View a PDF of the paper titled The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey, by Saurav Pawar and 5 other authors View PDF 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. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2401.07872 [cs.CL]   (or arXiv:2401.07872v1 [cs.CL] for this version)   https://doi.org/10.48550/arXiv.2401.07872 Focus to learn more arXiv-issued DOI via DataCite Submission history From: S.M Towhidul Islam Tonmoy [ view email ] [v1] Mon, 15 Jan 2024 18:07:21 UTC (8,691 KB) Full-text links: Access Paper: View a PDF of the paper titled The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey, by Saurav Pawar and 5 other authors View PDF TeX Source view license Current browse context: cs.CL < prev   |   next > new | recent | 2024-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar 1 blog link ( what is this? ) export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )