An Evaluation of Context Length Extrapolation in Long Code via Positional Embeddings and Efficient Attention
This work addresses a bottleneck in software engineering tools by enabling better generalization to long code, though it appears incremental as it evaluates existing methods rather than introducing new ones.
The paper tackled the problem of limited context length in large language models for long code sequences by evaluating zero-shot methods to improve positional embeddings and attention mechanisms, resulting in a thorough analysis of approaches for code completion tasks.
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.