Bridging the Gap: Introducing BigCodeBench for Evaluating Large Language Models in Real-World Programming Tasks

Embark on a journey to revolutionize AI through an open-source approach. Discover BigCodeBench, a new benchmark designed to assess large language models' programming capabilities in challenging real-world tasks, incorporating diverse libraries and function calls.

Published 1 year ago on huggingface.co

Abstract

BigCodeBench is introduced as a benchmark to evaluate large language models (LLMs) on real-world programming tasks. It addresses concerns regarding the effectiveness of existing benchmarks like HumanEval. BigCodeBench consists of 1,140 function-level tasks involving multiple function calls and tools from 139 libraries aimed at challenging LLMs. The benchmark employs Pass@1 with greedy decoding to measure LLM performance. While LLMs struggle on BigCodeBench tasks compared to humans, the best model achieves a calibrated Pass@1 of 61.1% on BigCodeBench-Complete. The article outlines the creation process of tasks, quality assurance, model evaluations, and future roadmap for BigCodeBench.

Results

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Discussion

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Relevance

This article is crucial as it unveils BigCodeBench, a significant benchmark addressing the limitations of existing assessments for large language models in real-world programming tasks. It highlights the need for improved evaluation methods and the challenges faced by LLMs in practical coding scenarios.

Applicability

If you are utilizing large language models in your projects, you should consider evaluating them using BigCodeBench to assess their performance in solving practical and complex programming tasks. By leveraging this benchmark, you can gain insights into the capabilities and limitations of your models and identify areas for improvement.

Risks

One risk to consider is that large language models may struggle to perform well on BigCodeBench tasks, showcasing their limitations in handling real-world programming challenges. Additionally, the complexity of tasks and the need for diverse library dependencies could pose challenges for model evaluation and improvement.

Conclusion

Looking ahead, the article suggests future enhancements for BigCodeBench, such as expanding beyond Python, increasing rigorousness in evaluations, assessing model generalization to unseen tools, adapting to evolving libraries, and exploring LLMs as agents. These trends indicate a continual evolution in benchmarking practices to better understand and harness the capabilities of large language models in programming tasks.

References

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BigCodeBench: Benchmarking Large Language Models on Solving Practical and Challenging Programming Tasks
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