CodeGolf Bench: A Multi-Language Benchmark for Evaluating Concise Code Generation Capabilities of Large Language Models
arXiv:2605.30394v1 Announce Type: cross Abstract: This paper introduces Code Bench, a benchmark capable of evaluating Large Language Models (LLMs) concise code generation abilities in 60 programming languages. Based on code golf, a recreational programming competition focused on minimal character or byte solutions, the benchmark provides a distinctive measure of LLMs ability to produce efficient, concise code. Unlike existing benchmarks limited by fixed problem sets and language coverage, CodeGolf Bench leverages the code.golf platform to provide new problems and live human performance baselin
The rapid advancement and widespread adoption of Large Language Models necessitate increasingly sophisticated and granular methods for evaluating their capabilities, especially concerning code generation quality and efficiency.
This benchmark provides a critical tool for developers and researchers to accurately measure and improve the code conciseness and multi-language proficiency of LLMs, directly impacting their real-world utility in software development.
The introduction of CodeGolf Bench shifts the standard for evaluating code-generating LLMs from mere functional correctness to also emphasize efficiency and brevity across a broad spectrum of programming languages.
- · LLM developers focused on code generation
- · Programming language communities
- · Software development platforms incorporating LLMs
- · Code golf enthusiasts
- · LLMs that generate verbose or inefficient code
- · Companies relying on less rigorous LLM code generation benchmarks
Improved benchmarks lead to a competitive acceleration in LLM code generation capabilities, specifically in conciseness and multi-language support.
More concise and efficient code generated by LLMs could reduce computational costs and improve software performance in various applications.
The pursuit of 'code golf' style efficiency in LLMs might influence programming language design, favoring constructs that enable more compact expressions.
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Read at arXiv cs.AI