An Evaluation of Context Length Extrapolation in Long Code via Positional Embeddings and Efficient Attention

• URL: http://arxiv.org/abs/2602.21800v1
• Date: Wed, 25 Feb 2026 11:27:34 GMT
• Title: An Evaluation of Context Length Extrapolation in Long Code via Positional Embeddings and Efficient Attention
• Authors: Madhusudan Ghosh, Rishabh Gupta,
• Abstract summary: Large language models (LLMs) have led to a significant increase in automated tools in the software engineering.<n>Despite these advancements, its effectiveness is constrained by fixed context lengths.<n>We investigate zero-shot, inference-only methods aimed at improving position encodings and optimizing attention mechanisms.
• Score: 4.307992396152879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of large language models (LLMs) has led to a significant increase in automated tools in the software engineering, capable of performing various code-related tasks such as code generation, completion, and translation. Despite these advancements, its effectiveness is constrained by fixed context lengths, limiting its ability to generalize across long, domain-specific code sequences. To address this challenge, we investigate zero-shot, inference-only methods aimed at improving position encodings and optimizing attention mechanisms. Our goal is to provide a thorough analysis of current approaches that facilitate context length extrapolation in code, particularly in the context of long code completion tasks.

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