Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916405574631424 |
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| author | Lee, Seonghyeon Kim, Suyeon Jang, Joonwon Chon, Heejae Lee, Dongha Yu, Hwanjo |
| author_facet | Lee, Seonghyeon Kim, Suyeon Jang, Joonwon Chon, Heejae Lee, Dongha Yu, Hwanjo |
| contents | We study the code generation behavior of instruction-tuned models built on top of code pre-trained language models when they could access an auxiliary function to implement a function. We design several ways to provide auxiliary functions to the models by adding them to the query or providing a response prefix to incorporate the ability to utilize auxiliary functions with the instruction-following capability. Our experimental results show the effectiveness of combining the base models' auxiliary function utilization ability with the instruction following ability. In particular, the performance of adopting our approaches with the open-sourced language models surpasses that of the recent powerful proprietary language models, i.e., gpt-4o. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13928 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation Lee, Seonghyeon Kim, Suyeon Jang, Joonwon Chon, Heejae Lee, Dongha Yu, Hwanjo Software Engineering Artificial Intelligence Computation and Language We study the code generation behavior of instruction-tuned models built on top of code pre-trained language models when they could access an auxiliary function to implement a function. We design several ways to provide auxiliary functions to the models by adding them to the query or providing a response prefix to incorporate the ability to utilize auxiliary functions with the instruction-following capability. Our experimental results show the effectiveness of combining the base models' auxiliary function utilization ability with the instruction following ability. In particular, the performance of adopting our approaches with the open-sourced language models surpasses that of the recent powerful proprietary language models, i.e., gpt-4o. |
| title | Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation |
| topic | Software Engineering Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2409.13928 |