NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts
(2405.04520)Abstract
LLMs have manifested strong ability to generate codes for productive activities. However, current benchmarks for code synthesis, such as HumanEval, MBPP, and DS-1000, are predominantly oriented towards introductory tasks on algorithm and data science, insufficiently satisfying challenging requirements prevalent in real-world coding. To fill this gap, we propose NaturalCodeBench (NCB), a challenging code benchmark designed to mirror the complexity and variety of scenarios in real coding tasks. NCB comprises 402 high-quality problems in Python and Java, meticulously selected from natural user queries from online coding services, covering 6 different domains. Noting the extraordinary difficulty in creating testing cases for real-world queries, we also introduce a semi-automated pipeline to enhance the efficiency of test case construction. Comparing with manual solutions, it achieves an efficiency increase of more than 4 times. Our systematic experiments on 39 LLMs find that performance gaps on NCB between models with close HumanEval scores could still be significant, indicating a lack of focus on practical code synthesis scenarios or over-specified optimization on HumanEval. On the other hand, even the best-performing GPT-4 is still far from satisfying on NCB. The evaluation toolkit and development set are available at https://github.com/THUDM/NaturalCodeBench.
Overview
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NaturalCodeBench (NCB) is introduced as a new benchmark designed to evaluate LLMs in coding tasks, improving upon existing benchmarks by focusing on real-world complexities and user-derived problems.
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NCB leverages a semi-automated pipeline utilizing GPT-4 for initial problem and solution generation, which significantly speeds up the benchmark development and offers a more realistic and diversified set of challenges across multiple programming languages and domains.
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Performance evaluation on NCB with 39 LLMs showed varied results, highlighting the benchmark's challenging nature and the potential for future enhancements in AI-driven coding tools to meet real-world needs more effectively.
Exploring NaturalCodeBench: A New Benchmark for Code Synthesis from LLMs
Introduction to NaturalCodeBench (NCB)
The field of AI-generated code has grown significantly with tools and models being developed to automate and assist in coding tasks, leveraging the prowess of LLMs. Despite robust benchmarks like HumanEval and MBPP, there's a recognized need for more intricate and real-world reflective testing environments, and that's where NaturalCodeBench (NCB) comes in.
NCB is characterized by its closer alignment with real-world coding challenges. It consists of 402 problems across Python and Java, gathered from actual user queries in online coding services. This makes NCB not just another benchmark but a significant step towards understanding LLMs' readiness for real-world applications.
Key Features of NCB
- Real-world Complexity: NCB problems are derived from natural user queries which makes it a good representative of the kinds of problems coders solve in real environments.
- Diverse Domains Covered: It includes categories like software, front-end, system administration, and AI, moving beyond basic algorithmic tasks.
- Bilingual Support: The problems are available in both English and Chinese, addressing a wider user base and testing language flexibility in coding LLMs.
Semi-Automated Pipeline for Increased Efficiency
Developing a benchmark as extensive and diverse as NCB is no small feat. It demands creating and testing numerous problems and solutions. The introduction of a semi-automated pipeline in NCB construction is a game-changer:
- Automation Aided by GPT-4: The first draft of solutions and test cases is generated by GPT-4, followed by human review and correction. This significantly speeds up the initial creation phase.
- Fourfold Efficiency Improvement: This method of using an AI-assisted pipeline reportedly enhances the construction speed by over four times compared to a fully manual approach.
Insights from LLM Performance on NCB
When 39 different LLMs were tested against NCB, it revealed informative performance disparities:
- Significant Gaps Noticed: Even models with similar performances on simpler benchmarks like HumanEval showed varied results on NCB, highlighting NCB's challenging nature.
- Room for Improvement: The best performing model, GPT-4, only achieved about a 53% pass rate, indicating significant potential for future improvements in AI-driven code synthesis tools.
Potential Areas for Future Development
The initial findings from NCB suggest several pathways for future development in code synthesis:
- Enhanced Model Training: The variations in performance across different benchmarks underline the necessity for models to train in more diverse and complex environments.
- Focus on Practical Application: Models need further tuning to handle the types of complex, file-driven, multi-faceted problems that NCB presents, which mirrors real-world coding scenarios more accurately than existing benchmarks.
Conclusion
NCB stands out by pushing the envelope on what it means to test AI in the realm of coding. Its real-world-derived problems and comprehensive coverage across multiple domains and languages make it a pioneering tool in evaluating the practical utility of LLMs in code synthesis. This benchmark not only tests the current capabilities but also sets a path for the evolution of coding LLMs towards handling real and complex engineering problems efficiently. As AI continues to seep into various facets of technology, tools like NCB ensure that this integration is both seamless and effective, paving the way for more AI-assisted coding environments in the future.
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