LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctness

TL;DR

This paper introduces ool, a novel framework that guides large language models to generate code optimized for both efficiency and correctness by combining logical exploration and implementation refinement, outperforming existing methods.

Contribution

The paper presents a new paradigm for code generation that emphasizes efficiency first, using a two-domain approach and synthetic testing to ensure correctness, advancing beyond prior incremental improvements.

Findings

Achieves state-of-the-art efficiency in code generation benchmarks.

Balances efficiency and correctness effectively across multiple LLMs.

Demonstrates consistent improvements over existing methods.

Abstract

Large Language Models (LLMs), particularly Code LLMs, have demonstrated impressive performance in code generation. Current research primarily focuses on the correctness of generated code, while efficiency remains less explored. Recent works have focused on modifying the initial version of the code to improve its efficiency. However, such refinements are limited by the algorithmic design and overall logic of the initial code, resulting in only incremental improvements. In contrast, when human developers write high-quality code, they typically begin by designing several potential solutions at the logical level, evaluating various algorithms and their complexities, and then proceeding to implement and optimize the solution. In this study, we introduce \tool: \uline{L}arge \uline{L}anguage \uline{M}odel for Code \uline{Effi}ciency, a novel framework that enables LLMs to generate code that balances both efficiency and correctness. Specifically, \tool divides the efficiency optimization process into two domains: algorithmic exploration in the logic domain and implementation optimization in the code domain. The correctness of the code is then guaranteed through a synthetic test case refinement process. This approach, which prioritizes efficiency before ensuring correctness, offers a new paradigm for efficient code generation. Experiments demonstrate that \tool consistently improves both efficiency and correctness, achieving new state-of-the-art performance in code efficiency benchmarks across various LLM backbones.