BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
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arXiv
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| Natura: | Preprint |
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2024
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| author | Zhuo, Terry Yue Vu, Minh Chien Chim, Jenny Hu, Han Yu, Wenhao Widyasari, Ratnadira Yusuf, Imam Nur Bani Zhan, Haolan He, Junda Paul, Indraneil Brunner, Simon Gong, Chen Hoang, Thong Zebaze, Armel Randy Hong, Xiaoheng Li, Wen-Ding Kaddour, Jean Xu, Ming Zhang, Zhihan Yadav, Prateek Jain, Naman Gu, Alex Cheng, Zhoujun Liu, Jiawei Liu, Qian Wang, Zijian Hui, Binyuan Muennighoff, Niklas Lo, David Fried, Daniel Du, Xiaoning de Vries, Harm Von Werra, Leandro |
| author_facet | Zhuo, Terry Yue Vu, Minh Chien Chim, Jenny Hu, Han Yu, Wenhao Widyasari, Ratnadira Yusuf, Imam Nur Bani Zhan, Haolan He, Junda Paul, Indraneil Brunner, Simon Gong, Chen Hoang, Thong Zebaze, Armel Randy Hong, Xiaoheng Li, Wen-Ding Kaddour, Jean Xu, Ming Zhang, Zhihan Yadav, Prateek Jain, Naman Gu, Alex Cheng, Zhoujun Liu, Jiawei Liu, Qian Wang, Zijian Hui, Binyuan Muennighoff, Niklas Lo, David Fried, Daniel Du, Xiaoning de Vries, Harm Von Werra, Leandro |
| contents | Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately understanding complex instructions. Fulfilling both of these characteristics can pose a great challenge for LLMs.To assess how well LLMs can solve challenging and practical tasks via programs, we introduce BigCodeBench, a benchmark that challenges LLMs to invoke multiple function calls as tools from 139 libraries and 7 domains for 1,140 fine-grained tasks. To evaluate LLMs rigorously, each task encompasses 5.6 test cases with an average branch coverage of 99%. In addition, we propose a natural-language-oriented variant of BigCodeBench, BigCodeBench-Instruct, that automatically transforms the original docstrings into short instructions only with essential information. Our extensive evaluation of 60 LLMs shows that LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%. The results underscore the need for further advancements in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15877 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions Zhuo, Terry Yue Vu, Minh Chien Chim, Jenny Hu, Han Yu, Wenhao Widyasari, Ratnadira Yusuf, Imam Nur Bani Zhan, Haolan He, Junda Paul, Indraneil Brunner, Simon Gong, Chen Hoang, Thong Zebaze, Armel Randy Hong, Xiaoheng Li, Wen-Ding Kaddour, Jean Xu, Ming Zhang, Zhihan Yadav, Prateek Jain, Naman Gu, Alex Cheng, Zhoujun Liu, Jiawei Liu, Qian Wang, Zijian Hui, Binyuan Muennighoff, Niklas Lo, David Fried, Daniel Du, Xiaoning de Vries, Harm Von Werra, Leandro Software Engineering Artificial Intelligence Computation and Language Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately understanding complex instructions. Fulfilling both of these characteristics can pose a great challenge for LLMs.To assess how well LLMs can solve challenging and practical tasks via programs, we introduce BigCodeBench, a benchmark that challenges LLMs to invoke multiple function calls as tools from 139 libraries and 7 domains for 1,140 fine-grained tasks. To evaluate LLMs rigorously, each task encompasses 5.6 test cases with an average branch coverage of 99%. In addition, we propose a natural-language-oriented variant of BigCodeBench, BigCodeBench-Instruct, that automatically transforms the original docstrings into short instructions only with essential information. Our extensive evaluation of 60 LLMs shows that LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%. The results underscore the need for further advancements in this area. |
| title | BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions |
| topic | Software Engineering Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2406.15877 |