Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

• URL: http://arxiv.org/abs/2308.01240v1
• Date: Wed, 2 Aug 2023 15:54:22 GMT
• Title: Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation
• Authors: Zhiqiang Yuan, Junwei Liu, Qiancheng Zi, Mingwei Liu, Xin Peng, Yiling Lou
• Abstract summary: In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. For the zero-shot setting, instructed LLMs are very competitive on code comprehension and generation tasks. For the few-shot setting, we find that adding demonstration examples substantially helps instructed LLMs perform better.
• Score: 4.310519298899164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. We have the following main findings. First, for the zero-shot setting, instructed LLMs are very competitive on code comprehension and generation tasks and sometimes even better than small SOTA models specifically fine-tuned on each downstream task. We also find that larger instructed LLMs are not always better on code-related tasks. Second, for the few-shot setting, we find that adding demonstration examples substantially helps instructed LLMs perform better on most code comprehension and generation tasks; however, the examples would sometimes induce unstable or even worse performance. Furthermore, we find widely-used BM25-based shot selection strategy significantly outperforms the basic random selection or fixed selection only on generation problems. Third, for the fine-tuning setting, we find that fine-tuning could further improve the model performance on downstream code comprehension and generation tasks compared to the zero-shot/one-shot performance. In addition, after being fine-tuned on the same downstream task dataset, instructed LLMs outperform both the small SOTA models and similar-scaled LLMs without instruction tuning. Based on our findings, we further present practical implications on model and usage recommendation, performance and cost trade-offs, and future direction.

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