Introducing BigCodeBench by BigCode: The New Gold Standard for Evaluating Large Language Models on Real-World Coding Tasks
Addressing Limitations in Current Benchmarks
Current benchmarks like HumanEval have been criticized for their simplicity and lack of real-world applicability. BigCodeBench aims to fill this gap by rigorously evaluating Large Language Models (LLMs) on practical and challenging tasks.
Components and Capabilities
BigCodeBench is divided into two main components: BigCodeBench-Complete and BigCodeBench-Instruct. It challenges LLMs to follow user-oriented instructions and compose multiple function calls from diverse libraries, ensuring thorough evaluation.
Evaluation Framework and Leaderboard
BigCode provides a user-friendly framework accessible via PyPI, with detailed setup instructions and pre-built Docker images for code generation and execution. The performance of models on BigCodeBench is measured using calibrated Pass@1, a metric that assesses the percentage of tasks correctly solved on the first attempt.
Community Engagement and Future Developments
BigCode encourages the AI community to engage with BigCodeBench by providing feedback and contributing to its development. All artifacts related to BigCodeBench are open-sourced and available on platforms like GitHub and Hugging Face.
Conclusion
The release of BigCodeBench marks a significant milestone in evaluating LLMs for programming tasks. By providing a comprehensive and challenging benchmark, BigCode aims to push the boundaries of what these models can achieve, ultimately driving the field of AI in software development.
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