arXiv:2606.20517v1 Announce Type: new Abstract: LiveCodeBench (LCB) has recently become a widely adopted benchmark for evaluating large language models (LLMs) on code-generation tasks. By curating competitive programming problems, constantly adding fresh problems to the set, and filtering them by release dates, LCB provides contamination-aware evaluation and offers a holistic view of coding capability. However, LCB remains restricted to Python, leaving open the question of whether LLMs can generalize across the diverse programming languages required in real-world software engineering. We intro
The proliferation of Large Language Models (LLMs) in code generation necessitates more robust, generalized, and contamination-aware evaluation benchmarks for their real-world applicability.
A benchmark like Multi-LCB is crucial for measuring and improving LLM capabilities across diverse programming languages, which is essential for broad adoption in software engineering.
LLM evaluation for code generation is moving beyond single-language assessment towards multi-language generalization, providing a more comprehensive view of model performance.
- · Large Language Model developers
- · Companies adopting LLMs for code generation
- · Software engineers leveraging diverse programming languages
- · Academic researchers in AI/programming languages
- · LLMs with poor generalization across languages
- · Benchmarks restricted to single programming languages
Multi-LCB will enable more accurate and holistic assessment of LLM coding capabilities across various programming languages.
Improved evaluation will drive the development of more versatile and robust code-generating LLMs, capable of handling real-world, multi-language software projects.
The enhanced generalization of code-generating LLMs could accelerate developer productivity and the automation of software creation across a broader spectrum of industries.
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