Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs
TL;DR
Existing evaluations of code generation models often rely on simple pass rates, which fail to capture performance nuances across languages and problem types. A comprehensive evaluation was conducted on 9 open-source large language models (LLMs) using 2,707 LeetCode problems in 12 programming languages.
✦ Why It Matters
Engineers can better select code generation models based on nuanced performance insights across languages and problem types.
Key Takeaways
- Explore multilingual benchmarks for evaluating code generation models.
- Recognize that compile errors significantly impact model performance.
- Use execution-grounded assessments to uncover hidden trade-offs.
- Aim for higher correctness rates by analyzing failure modes.
How It Works
The evaluation methodology involved testing LLMs on a diverse set of coding problems across multiple languages, linking each problem to execution outcomes and static analysis signals. This comprehensive approach allows for a detailed understanding of model performance beyond simple pass rates.