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Context Length Extension Techniques in LLMs — arXiv Survey
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.
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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, 0.95) · S.M Towhidul Islam Tonmoy (person, 0.95) · S M Mehedi Zaman (person, 0.9) · Vinija Jain (person, 0.9) · Aman Chadha (person, 0.9) · Amitava Das (person, 0.9) · Large Language Models (technology, 1) · Context Length Extension (concept, 1) · Natural Language Processing (concept, 0.98) · arXiv (organization, 0.95)
Classification
reference · language en · status final
Provenance
claude-opus-4-6 via @stacklist/mcp-server@2.0.0, confidence 0.85, 2 Jul 2026