New LiveCodeBench Leaderboard for Comprehensive Code LLM Evaluation

Introducing the LiveCodeBench leaderboard, a new benchmark developed by researchers from top universities to assess LLMs' code generation abilities in a contamination-free manner.

Published 1 year ago on huggingface.co

Abstract

The article introduces the LiveCodeBench leaderboard, a benchmark created by UC Berkeley, MIT, and Cornell researchers to evaluate LLMs' code generation capabilities. LiveCodeBench collects coding problems from platforms like LeetCode, AtCoder, and CodeForces, providing scenarios such as code generation, self-repair, code execution, and test output prediction for a holistic assessment. Utilizing a Pass@1 metric, LiveCodeBench aims to prevent benchmark contamination by annotating problems with release dates. Noteworthy findings include the varying performance of models across scenarios and the effectiveness of GPT-4-Turbo, Claude-3-Opus, and Mistral-Large on different tasks.

Results

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Discussion

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Relevance

This article is crucial for understanding a new benchmark, LiveCodeBench, that can help you evaluate code generation capabilities effectively and prevent contamination. It offers insights into holistic coding assessment and presents opportunities to improve AI programming agents.

Applicability

If you are working with AI models, especially in code generation, self-repair, or natural language tasks, you should consider evaluating them using LiveCodeBench to get a comprehensive view of their performance and prevent benchmark contamination. You can contribute by submitting your results or collaborating on the project.

Risks

One potential risk to be aware of is the need to ensure your models perform well across various scenarios to avoid model-specific biases. Additionally, the evolving nature of AI benchmarks means staying updated on the latest methodologies and improvements to adapt your strategies accordingly.

Conclusion

The LiveCodeBench benchmark represents a step towards more reliable and unbiased model evaluations in the AI field. By adopting comprehensive evaluation practices like those offered by LiveCodeBench, you can stay ahead of the curve in AI development and ensure the effectiveness of your models in addressing diverse coding challenges.

References

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Introducing the LiveCodeBench Leaderboard - Holistic and Contamination-Free Evaluation of Code LLMs
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