NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Queries (2024.findings-acl)
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| Challenge: | Large language models (LLMs) generate code for productive activities, but current benchmarks for code synthesis are oriented towards introductory tasks on algorithm and data science. |
| Approach: | They propose a code benchmark to mirror the complexity and variety of scenarios in real-world coding tasks. |
| Outcome: | The proposed benchmark improves on 39 large language models with close HumanEval scores and achieves an efficiency increase of more than 4 times. |
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| Challenge: | Existing benchmarks focus on translating English prompts to multilingual codes or have been constrained to very limited natural languages (NLs). |
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| Challenge: | Large Language Models (LLMs) are increasingly used to judge code, but their reliability remains poorly understood. |
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